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We address the problem of coordination and control of Connected and Automated Vehicles (CAVs) in the presence of imperfect observations in mixed traffic environment.
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C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. Wu, “The surprising effectiveness of ppo in cooperative multi-agent games,”
2022
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L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig, “Safe learning in robotics: From learning-based control to safe reinforcement learning,”
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2020
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2021
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2021
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2021
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S. Lu, K. Zhang, T. Chen, T. Başar, and L. Horesh, “Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning,” vol. 35, no. 10, pp. 8767–8775, 2021
2021
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E. Salvato, G. Fenu, E. Medvet, and F. A. Pellegrino, “Crossing the reality gap: A survey on sim-to-real transferability of robot controllers in reinforcement learning,”
2021
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S. Han, H. Wang, S. Su, Y. Shi, and F. Miao, “Stable and efficient shapley value-based reward reallocation for multi-agent reinforcement learning of autonomous vehicles,” pp. 8765–8771, 2022
2022
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Y. Liang, Y. Sun, R. Zheng, and F. Huang, “Efficient adversarial training without attacking: Worst-case-aware robust reinforcement learning,”
2022
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Z. Zhang, S. Han, J. Wang, and F. Miao, “Spatial-temporal-aware safe multi-agent reinforcement learning of connected autonomous vehicles in challenging scenarios,” pp. 5574–5580, 2023
2023
Closest in time.
S. Gu, J. Grudzien Kuba, Y. Chen, Y. Du, L. Yang, A. Knoll, and Y. Yang, “Safe multi-agent reinforcement learning for multi-robot control,”
2023
Closest in time.
2023
Closest in time.
S. He, S. Han, S. Su, S. Han, S. Zou, and F. Miao, “Robust multi-agent reinforcement learning with state uncertainty,”
2023
Closest in time.
E. Sabouni, H. S. Ahmad, C. G. Cassandras, and W. Li, “Merging control in mixed traffic with safety guarantees: A safe sequencing policy with optimal motion control,” in
2023
Closest in time.
2024
Closest in time.