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Erschienen in:

12.08.2022

Intelligent Traffic Light via Policy-based Deep Reinforcement Learning

verfasst von: Yue Zhu, Mingyu Cai, Chris W. Schwarz, Junchao Li, Shaoping Xiao

Erschienen in: International Journal of Intelligent Transportation Systems Research | Ausgabe 3/2022

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Abstract

Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on a simulator of urban mobility. As a difference from existing works, a policy-based deep reinforcement learning method, Proximal Policy Optimization (PPO), is utilized rather than value-based methods such as Deep Q Network (DQN) and Double DQN (DDQN). First, the obtained optimal policy from PPO is compared to those from DQN and DDQN. It is found that the policy from PPO performs better than the others. Next, instead of fixed-interval traffic light phases, we adopt light phases with variable time intervals, which result in a better policy to pass the traffic flow. Then, the effects of environment and action disturbances are studied to demonstrate that the learning-based controller is robust. Finally, we consider unbalanced traffic flows and find that an intelligent traffic light can perform moderately well for the unbalanced traffic scenarios, although it learns the optimal policy from the balanced traffic scenarios only.

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github.com/YueZhu95/Intelligent-Traffic-Light-via-Reinforcement-Learning.
 
Literatur
10.
Zurück zum Zitat Cai, M., Hasanbeig, M., Xiao, S., Abate, A., Kan, Z. Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic. IEEE Robot. Autom. Lett. 6(4):7973–7980. (2021). http://arxiv.org/abs/2102.12855. Accessed April 8, 2021 Cai, M., Hasanbeig, M., Xiao, S., Abate, A., Kan, Z. Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic. IEEE Robot. Autom. Lett. 6(4):7973–7980. (2021). http://​arxiv.​org/​abs/​2102.​12855. Accessed April 8, 2021
11.
Zurück zum Zitat Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. The MIT Press, London (2018)MATH Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. The MIT Press, London (2018)MATH
25.
Zurück zum Zitat Hasselt H van, Guez, A., Silver, D.: Deep Reinforcement Learning with Double Q-Learning. In: AAAI’16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 30, 2094-2100 (2016) Hasselt H van, Guez, A., Silver, D.: Deep Reinforcement Learning with Double Q-Learning. In: AAAI’16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 30, 2094-2100 (2016)
27.
Zurück zum Zitat Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. 32, 807–814 (2010) Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning. 32, 807–814 (2010)
29.
Zurück zum Zitat Kakade, S., Langford, J.: Approximately optimal approximate reinforcement learning. In: In Proc. 19th International Conference on Machine Learning (2002) Kakade, S., Langford, J.: Approximately optimal approximate reinforcement learning. In: In Proc. 19th International Conference on Machine Learning (2002)
30.
Zurück zum Zitat Mnih, V., Badia, A.P., Mirza, M., et al.: Asynchronous Methods for Deep Reinforcement Learning. In: Balcan MF, Weinberger KQ, eds. Proceedings of The 33rd International Conference on Machine Learning. Vol 48. Proceedings of Machine Learning Research. New York, New York, USA: PMLR:1928–1937. https://proceedings.mlr.press/v48/mniha16.html (2016). Accessed November 23, 2020 Mnih, V., Badia, A.P., Mirza, M., et al.: Asynchronous Methods for Deep Reinforcement Learning. In: Balcan MF, Weinberger KQ, eds. Proceedings of The 33rd International Conference on Machine Learning. Vol 48. Proceedings of Machine Learning Research. New York, New York, USA: PMLR:1928–1937. https://​proceedings.​mlr.​press/​v48/​mniha16.​html (2016). Accessed November 23, 2020
31.
Zurück zum Zitat Schulman, J., Moritz, P., Levine, S., Jordan, M.I., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings. International Conference on Learning Representations, ICLR. https://sites.google.com/site/gaepapersupp (2016). Accessed November 23, 2020 Schulman, J., Moritz, P., Levine, S., Jordan, M.I., Abbeel, P.: High-dimensional continuous control using generalized advantage estimation. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings. International Conference on Learning Representations, ICLR. https://​sites.​google.​com/​site/​gaepapersupp (2016). Accessed November 23, 2020
32.
Metadaten
Titel
Intelligent Traffic Light via Policy-based Deep Reinforcement Learning
verfasst von
Yue Zhu
Mingyu Cai
Chris W. Schwarz
Junchao Li
Shaoping Xiao
Publikationsdatum
12.08.2022
Verlag
Springer US
Erschienen in
International Journal of Intelligent Transportation Systems Research / Ausgabe 3/2022
Print ISSN: 1348-8503
Elektronische ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-022-00321-5

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