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Traffic signal control is of critical importance for the effective use of transportation infrastructures.
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T. Kurutach, I. Clavera, Y. Duan, A. Tamar, and P. Abbeel, “Model-ensemble trust-region policy optimization,” International Conference on Learning Representations 2018 , 2018
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S. G. Rizzo, G. Vantini, and S. Chawla, “Time critic policy gradient methods for traffic signal control in complex and congested scenarios,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 1654–1664
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N. O. Lambert, D. S. Drew, J. Yaconelli, S. Levine, R. Calandra, and K. S. Pister, “Low-level control of a quadrotor with deep model-based reinforcement learning,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 4224–4230, 2019
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A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn, “Learning to adapt in dynamic, real-world environments through meta-reinforcement learning,” International Conference on Learning Representations , 2019
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L. Kaiser, M. Babaeizadeh, P. Milos, B. Osinski, R. H. Campbell, K. Czechowski, D. Erhan, C. Finn, P. Kozakowski, S. Levine et al. , “Model-based reinforcement learning for atari,” International Conference on Learning Representations , 2019
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A. Oroojlooy, M. Nazari, D. Hajinezhad, and J. Silva, “Attendlight: Universal attention-based reinforcement learning model for traffic signal control,” in Advances in Neural Information Processing Systems, 2020 , 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/29e48b79ae6fc68e9b6480b677453586-Abstract.html
2020
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X. Zang, H. Yao, G. Zheng, N. Xu, K. Xu, and Z. Li, “Metalight: Value-based meta-reinforcement learning for traffic signal control,” in Proceedings of the AAAI Conference on Artificial Intelligence , New York, NY, 2020, pp. 1153–1160
2020
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C. Chen, H. Wei, N. Xu, G. Zheng, M. Yang, Y. Xiong, K. Xu, and Z. Li, “Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control,” in Proceedings of the AAAI Conference on Artificial Intelligence , New York, NY, 2020, pp. 3414–3421
2020
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Z. Yu, S. Liang, L. Wei, Z. Jin, J. Huang, D. Cai, X. He, and X.-S. Hua, “Macar: Urban traffic light control via active multi-agent communication and action rectification,” in IJCAI=PRICAI , Yokahama, Japan, 2020, pp. 2491–2497
2020
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X. Wang, L. Ke, Z. Qiao, and X. Chai, “Large-scale traffic signal control using a novel multiagent reinforcement learning,” IEEE Transactions on Cybernetics , vol. 51, pp. 174–187, 2020
2020
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W. Genders and S. Razavi, “Policy analysis of adaptive traffic signal control using reinforcement learning,” Journal of Computing in Civil Engineering , vol. 34, no. 1, p. 04019046, 2020
2020
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H. Zhang, M. Kafouros, and Y. Yu, “Planlight: Learning to optimize traffic signal control with planning and iterative policy improvement,” IEEE Access , vol. 8, pp. 219 244–219 255, 2020
2020
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H. Zhang, C. Liu, W. Zhang, G. Zheng, and Y. Yu, “Generalight: Improving environment generalization of traffic signal control via meta reinforcement learning,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 1783–1792
2020
Later among the works it cites.
L. Zhu, P. Peng, Z. Lu, X. Wang, and Y. Tian, “Meta variationally intrinsic motivated reinforcement learning for decentralized traffic signal control,” arXiv e-prints , pp. arXiv–2101, 2021
2021
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