Optimizing Vehicular Networks with Variational Quantum Circuits-based Reinforcement Learning
Abstract: In vehicular networks (VNets), ensuring both road safety and dependable network connectivity is of utmost importance. Achieving this necessitates the creation of resilient and efficient decision-making policies that prioritize multiple objectives. In this paper, we develop a Variational Quantum Circuit (VQC)-based multi-objective reinforcement learning (MORL) framework to characterize efficient network selection and autonomous driving policies in a vehicular network (VNet). Numerical results showcase notable enhancements in both convergence rates and rewards when compared to conventional deep-Q networks (DQNs), validating the efficacy of the VQC-MORL solution.
- Z. Yan and H. Tabassum, “Reinforcement learning for joint v2i network selection and autonomous driving policies,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 2022, pp. 1241–1246.
- A. Kesting, M. Treiber, and D. Helbing, “Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 368, no. 1928, pp. 4585–4605, 2010.
- M. T. Hossan and H. Tabassum, “Mobility-aware performance in hybrid rf and terahertz wireless networks,” IEEE Transactions on Communications, vol. 70, no. 2, pp. 1376–1390, 2022.
- E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env, 2018.
- Z. Yan, W. Jaafar, B. Selim, and H. Tabassum, “Multi-uav speed control with collision avoidance and handover-aware cell association: Drl with action branching,” in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 5067–5072.
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