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Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges.
A survey on traffic signal control methods
Wei, H., Zheng, G., Gayah, V., and Li, Z · 1904
Earlier work this paper cites.
Diagnosing reinforcement learning for traffic signal control
Zheng, G., Zang, X., Xu, N., Wei, H., Yu, Z., Gayah, V., Xu, K., and Li, Z · 1905
Earlier work this paper cites.
CoLight: Learning network-level cooperation for traffic signal control
Wei, H., Xu, N., Zhang, H., Zheng, G., Zang, X., Chen, C., Zhang, W., Zhu, Y., Xu, K., and Li, Z · 1922
Earlier work this paper cites.
Learning phase competition for traffic signal control
Zheng, G., Xiong, Y., Zang, X., Feng, J., Wei, H., Zhang, H., Li, Y., Xu, K., and Li, Z · 1972
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The SCOOT on-line traffic signal optimisation technique
Hunt, P., Robertson, D., Bretherton, R., and Royle, M. C · 1982
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SCATS: A traffic responsive method of controlling urban traffic
Lowrie, P · 1990
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The green wave model of two-dimensional traffic: Transitions in the flow properties and in the geometry of the traffic jam
Török, J. and Kertész, J · 1996
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Traffic signal timing manual
Koonce, P. and Rodegerdts, L · 2008
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Self-organizing traffic lights: A realistic simulation
Cools, S.-B., Gershenson, C., and D’Hooghe, B · 2013
Earlier work this paper cites.
Max pressure control of a network of signalized intersections
Varaiya, P · 2013
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Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Kulkarni, T. D., Narasimhan, K., Saeedi, A., and Tenenbaum, J · 2016
Earlier work this paper cites.
Dueling network architectures for deep reinforcement learning
Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., and Freitas, N · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
Cited alongside, same era.
IntelliLight: A reinforcement learning approach for intelligent traffic light control
Wei, H., Zheng, G., Yao, H., and Li, Z · 2018
Cited alongside, same era.
Conservative Q-learning for offline reinforcement learning
Kumar, A., Zhou, A., Tucker, G., and Levine, S · 2020
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AttendLight: Universal attention-based reinforcement learning model for traffic signal control
Oroojlooy, A., Nazari, M., Hajinezhad, D., and Silva, J · 2020
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MetaLight: Value-based meta-reinforcement learning for traffic signal control
Zang, X., Yao, H., Zheng, G., Xu, N., Xu, K., and Li, Z · 2020
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
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PRGLight: A novel traffic light control framework with pressure-based-reinforcement learning and graph neural network
Chenguang, Z., Xiaorong, H., and Gang, W · 2021
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Off-policy deep reinforcement learning without exploration
Fujimoto, S., Meger, D., and Precup, D · 2019
Cited alongside, same era.
Stabilizing off-policy Q-learning via bootstrapping error reduction
Kumar, A., Fu, J., Soh, M., Tucker, G., and Levine, S · 2019
Cited alongside, same era.
Behavior regularized offline reinforcement learning
Wu, Y., Tucker, G., and Nachum, O · 2019
Cited alongside, same era.
Learning traffic signal control from demonstrations
Xiong, Y., Zheng, G., Xu, K., and Li, Z · 2019
Cited alongside, same era.
CityFlow: A multi-agent reinforcement learning environment for large scale city traffic scenario
Zhang, H., Feng, S., Liu, C., Ding, Y., Zhu, Y., Zhou, Z., Zhang, W., Yu, Y., Jin, H., and Li, Z · 2019
Cited alongside, same era.
Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control
Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., Xu, K., and Li, Z · 2020
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Fujimoto, S. and Gu, S. S · 2021
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Offline reinforcement learning as one big sequence modeling problem
Janner, M., Li, Q., and Levine, S · 2021
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Hierarchically and cooperatively learning traffic signal control
Xu, B., Wang, Y., Wang, Z., Jia, H., and Lu, Z · 2021
Later among the works it cites.
Expression might be enough: Representing pressure and demand for reinforcement learning based traffic signal control
Zhang, L., Wu, Q., Shen, J., Lü, L., Du, B., and Wu, J · 2022
Later among the works it cites.
Eigensubspace of temporal-difference dynamics and how it improves value approximation in reinforcement learning
He, Q., Zhou, T., Fang, M., and Maghsudi, S · 2023
Closest in time.
Leveraging queue length and attention mechanisms for enhanced traffic signal control optimization
Zhang, L., Xie, S., and Deng, J · 2023
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