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Interpretable Generative Adversarial Imitation Learning
Published 15 Feb 2024 in cs.LG, cs.SY, and eess.SY | (2402.10310v1)
Abstract: Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods is their lack of interpretability, particularly in understanding the specific task the learning agent aims to accomplish. In this paper, we propose a novel imitation learning method that combines Signal Temporal Logic (STL) inference and control synthesis, enabling the explicit representation of the task as an STL formula. This approach not only provides a clear understanding of the task but also allows for the incorporation of human knowledge and adaptation to new scenarios through manual adjustments of the STL formulae. Additionally, we employ a Generative Adversarial Network (GAN)-inspired training approach for both the inference and the control policy, effectively narrowing the gap between the expert and learned policies. The effectiveness of our algorithm is demonstrated through two case studies, showcasing its practical applicability and adaptability.
- Classification of time-series data using boosted decision trees. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1263–1268. IEEE, 2022.
- Abien Fred Agarap. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375, 2018.
- Q-learning for robust satisfaction of signal temporal logic specifications. In 2016 IEEE 55th Conference on Decision and Control (CDC), pages 6565–6570. IEEE, 2016.
- Parametric identification of temporal properties. In Runtime Verification: Second International Conference, RV 2011, San Francisco, CA, USA, September 27-30, 2011, Revised Selected Papers 2, pages 147–160. Springer, 2012.
- Uncertainty-aware signal temporal logic inference. In International Workshop on Numerical Software Verification, pages 61–85. Springer, 2021.
- Principles of model checking. MIT press, 2008.
- End-to-end differentiable adversarial imitation learning. In International Conference on Machine Learning, pages 390–399. PMLR, 2017.
- Offline and online learning of signal temporal logic formulae using decision trees. ACM Transactions on Cyber-Physical Systems, 5(3):1–23, 2021.
- Interpretable fault diagnosis of rolling element bearings with temporal logic neural network, 2022.
- Robust satisfaction of temporal logic over real-valued signals. In International Conference on Formal Modeling and Analysis of Timed Systems, pages 92–106. Springer, 2010.
- CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pages 1–16, 2017.
- A smooth robustness measure of signal temporal logic for symbolic control. IEEE Control Systems Letters, 5(1):241–246, 2020.
- Deep learning. MIT press, 2016.
- Generative adversarial networks. Communications of the ACM, 63(11):139–144, 2020.
- Control from signal temporal logic specifications with smooth cumulative quantitative semantics. In 2019 IEEE 58th Conference on Decision and Control (CDC), pages 4361–4366. IEEE, 2019.
- Generative adversarial imitation learning. Advances in neural information processing systems, 29, 2016.
- Mining parametric temporal logic properties in model-based design for cyber-physical systems. International Journal on Software Tools for Technology Transfer, 20:79–93, 2018.
- Mining requirements from closed-loop control models. In Proceedings of the 16th international conference on Hybrid systems: computation and control, pages 43–52, 2013.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
- Temporal logics for learning and detection of anomalous behavior. IEEE Transactions on Automatic Control, 62(3):1210–1222, 2016.
- Semi-supervised trajectory-feedback controller synthesis for signal temporal logic specifications. In 2022 American Control Conference (ACC), pages 178–185. IEEE, 2022.
- Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods. The International Journal of Robotics Research, 42(6):356–370, 2023.
- Learning signal temporal logic through neural network for interpretable classification. In 2023 American Control Conference (ACC), pages 1907–1914. IEEE, 2023.
- Recurrent neural network controllers for signal temporal logic specifications subject to safety constraints. IEEE Control Systems Letters, 6:91–96, 2021.
- Safe model-based control from signal temporal logic specifications using recurrent neural networks. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 12416–12422. IEEE, 2023.
- Monitoring temporal properties of continuous signals. In International Symposium on Formal Techniques in Real-Time and Fault-Tolerant Systems, pages 152–166. Springer, 2004.
- Interpretable classification of time-series data using efficient enumerative techniques. In Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control, pages 1–10, 2020.
- Algorithms for inverse reinforcement learning. In Icml, volume 1, page 2, 2000.
- Smooth operator: Control using the smooth robustness of temporal logic. In 2017 IEEE Conference on Control Technology and Applications (CCTA), pages 1235–1240. IEEE, 2017.
- Dean A Pomerleau. Efficient training of artificial neural networks for autonomous navigation. Neural computation, 3(1):88–97, 1991.
- Model predictive control with signal temporal logic specifications. In 53rd IEEE Conference on Decision and Control, pages 81–87. IEEE, 2014.
- Robust temporal logic model predictive control. In 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 772–779. IEEE, 2015.
- Robust imitation of diverse behaviors. Advances in Neural Information Processing Systems, 30, 2017.
- Generalized simulated annealing algorithm and its application to the thomson model. Physics Letters A, 233(3):216–220, 1997.
- Advisory temporal logic inference and controller design for semiautonomous robots. IEEE Transactions on Automation Science and Engineering, 16(1):459–477, 2018.
- Worst-case satisfaction of stl specifications using feedforward neural network controllers: a lagrange multipliers approach. ACM Transactions on Embedded Computing Systems (TECS), 18(5s):1–20, 2019.
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