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Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
Published 11 Mar 2024 in cs.LG, cs.AI, and cs.RO | (2403.06524v1)
Abstract: We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.
- Moreno, G., Nicolazzi, L.C., Vieira, R.D.S., Martins, D.: Stability of long combination vehicles. International journal of heavy vehicle systems 25 (2018) Yang et al. [2015] Yang, D., Qiu, X., Yu, D., Sun, R., Pu, Y.: A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect. Physica A: Statistical Mechanics and its Applications 424 (2015) Nilsson et al. [2018] Nilsson, P., Laine, L., Sandin, J., Jacobson, B., Eriksson, O.: On actions of long combination vehicle drivers prior to lane changes in dense highway traffic – a driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour 55 (2018) Grislis [2010] Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, D., Qiu, X., Yu, D., Sun, R., Pu, Y.: A cellular automata model for car–truck heterogeneous traffic flow considering the car–truck following combination effect. Physica A: Statistical Mechanics and its Applications 424 (2015) Nilsson et al. [2018] Nilsson, P., Laine, L., Sandin, J., Jacobson, B., Eriksson, O.: On actions of long combination vehicle drivers prior to lane changes in dense highway traffic – a driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour 55 (2018) Grislis [2010] Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Nilsson, P., Laine, L., Sandin, J., Jacobson, B., Eriksson, O.: On actions of long combination vehicle drivers prior to lane changes in dense highway traffic – a driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour 55 (2018) Grislis [2010] Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Nilsson, P., Laine, L., Sandin, J., Jacobson, B., Eriksson, O.: On actions of long combination vehicle drivers prior to lane changes in dense highway traffic – a driving simulator study. Transportation Research Part F: Traffic Psychology and Behaviour 55 (2018) Grislis [2010] Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Grislis, A.: Longer combination vehicles and road safety. Transport 25 (2010) Shaout et al. [2011] Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shaout, A., Colella, D., Awad, S.: Advanced driver assistance systems-past, present and future. In: International Computer Engineering Conference (2011). IEEE Jiménez et al. [2016] Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. 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Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. 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[2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. 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In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. 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[2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. 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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Jiménez, F., Naranjo, J.E., Anaya, J.J., GarcÃa, F., Ponz, A., Armingol, J.M.: Advanced driver assistance system for road environments to improve safety and efficiency. Transportation research procedia 14 (2016) Xiao and Gao [2010] Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. 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[2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Vehicle system dynamics (2010) Sutton and Barto [2018] Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. (2018) Sallab et al. [2017] Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Physical review E 62(2), 1805 (2000) Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. arXiv preprint arXiv:1704.02532 (2017) Shalev-Shwartz et al. [2016] Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Shalev-Shwartz, S., Shammah, S., Shashua, A.: Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295 (2016) Kiran et al. [2021] Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. 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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. 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[2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Kiran, B.R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A.A., Yogamani, S., Pérez, P.: Deep reinforcement learning for autonomous driving: A survey. Transactions on Intelligent Transportation Systems 23 (2021) Hoel et al. [2020] Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. 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Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. 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Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J., Wolff, K., Laine, L.: Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In: Intelligent Vehicles Symposium (2020). IEEE Desjardins and Chaib-draa [2011] Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
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[2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 12 (2011) Zhao et al. [2013] Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Zhao, D., Wang, B., Liu, D.: A supervised actor–critic approach for adaptive cruise control. Soft Computing 17 (2013) Konda and Tsitsiklis [1999] Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. 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[2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. 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[2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. 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Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. 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[2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Konda, V., Tsitsiklis, J.: Actor-critic algorithms. In: Solla, S., Leen, T., Müller, K. (eds.) Advances in Neural Information Processing Systems (1999) Haarnoja et al. [2018] Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (2018) Lin et al. [2021] Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. 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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. 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Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lin, Y., McPhee, J., Azad, N.L.: Comparison of deep reinforcement learning and model predictive control for adaptive cruise control. IEEE Transactions on Intelligent Vehicles 6 (2021) Das and Won [2021] Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Das, L.C., Won, M.: Saint-acc: Safety-aware intelligent adaptive cruise control for autonomous vehicles using deep reinforcement learning. In: ICML (2021) Yang et al. [2020] Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Yang, J., Liu, X., Liu, S., Chu, D., Lu, L., Wu, C.: Longitudinal tracking control of vehicle platooning using ddpg-based pid. In: 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) (2020) Hoel [2020] Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. [2015] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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[2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
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In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Hoel, C.-J.E.: Source code for ‘Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation’ (2020). https://github.com/carljohanhoel/BayesianRLForAutonomousDriving Mnih et al. 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[2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A.K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015) Mnih et al. [2016] Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 48, pp. 1928–1937. PMLR, New York, New York, USA (2016). https://proceedings.mlr.press/v48/mniha16.html Schulman et al. [2017] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal Policy Optimization Algorithms (2017) Pathare et al. [2023] Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. 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Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. 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Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
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Physical review E 62(2), 1805 (2000) Pathare, D., Laine, L., Chehreghani, M.H.: Improved tactical decision making and control architecture for autonomous truck in sumo using reinforcement learning. In: 2023 IEEE International Conference on Big Data (BigData), pp. 5321–5329 (2023). https://doi.org/10.1109/BigData59044.2023.10386803 Bengio et al. [2009] Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. 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[1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48 (2009) Narvekar et al. [2020] Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Narvekar, S., Peng, B., Leonetti, M., Sinapov, J., Taylor, M.E., Stone, P.: Curriculum learning for reinforcement learning domains: A framework and survey. The Journal of Machine Learning Research 21(1), 7382–7431 (2020) Anzalone et al. [2022] Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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Physical review E 62(2), 1805 (2000) Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. 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Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. 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[2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. 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In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. 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Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Anzalone, L., Barra, P., Barra, S., Castiglione, A., Nappi, M.: An end-to-end curriculum learning approach for autonomous driving scenarios. IEEE Transactions on Intelligent Transportation Systems 23(10), 19817–19826 (2022) https://doi.org/10.1109/TITS.2022.3160673 Liu et al. [2023] Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. 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[2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Liu, J., Li, H., Yang, Z., Dang, S., Huang, Z.: Deep dense network-based curriculum reinforcement learning for high-speed overtaking. IEEE Intelligent Transportation Systems Magazine 15(1), 453–466 (2023) https://doi.org/10.1109/MITS.2022.3174410 Song et al. [2021] Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Song, Y., Lin, H., Kaufmann, E., Durr, P., Scaramuzza, D.: Autonomous overtaking in gran turismo sport using curriculum reinforcement learning, pp. 9403–9409 (2021). https://doi.org/10.1109/ICRA48506.2021.9561049 Lopez et al. [2018] Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., Wießner, E.: Microscopic traffic simulation using sumo. In: International Conference on Intelligent Transportation Systems (2018) Krauss et al. [1997] Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Krauss, S., Wagner, P., Gawron, C.: Metastable states in a microscopic model of traffic flow. Phys. Rev. E 55 (1997) Erdmann [2014] Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Erdmann, J.: Lane-changing model in sumo. In: Proceedings of the SUMO2014 Modeling Mobility with Open Data. Reports of the DLR-Institute of Transportation SystemsProceedings, vol. 24. Deutsches Zentrum für Luft- und Raumfahrt e.V., ??? (2014) Raffin et al. [2021] Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research 22 (2021) Svensson et al. [2023] Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Svensson, H.G., Tyrchan, C., Engkvist, O., Chehreghani, M.H.: Utilizing Reinforcement Learning for de novo Drug Design (2023) Treiber et al. [2000] Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000) Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
- Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Physical review E 62(2), 1805 (2000)
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