CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation-Reference-Cited by-同舟云学术

CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Liang Chumeng¹^ORCID,Huang Zherui¹^ORCID,Liu Yicheng¹^ORCID,Liu Zhanyu¹^ORCID,Zheng Guanjie¹^ORCID,Shi Hanyuan²^ORCID,Wu Kan³^ORCID,Du Yuhao²^ORCID,Li Fuliang⁴^ORCID,Li Zhenhui Jessie⁵^ORCID

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Independent Researcher, Hangzhou, China

3. Research Center for Intelligent Transportation, Zhejiang Lab, Hangzhou, China

4. Baidu, Beijing, China

5. Yunqi Academy of Engineering, Hangzhou, China

Funder

National Natural Science Foundation of China award

National Key Research and Development Program of China award

Provincial Key Research and Development Program of Zhejiang award

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599789

Reference30 articles.

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2. Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

3. QarSUMO

4. TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

5. Seung-Bae Cools , Carlos Gershenson , and Bart D'Hooghe . 2013. Self-organizing traffic lights: A realistic simulation . In Advances in applied self-organizing systems . Springer , 45--55. Seung-Bae Cools, Carlos Gershenson, and Bart D'Hooghe. 2013. Self-organizing traffic lights: A realistic simulation. In Advances in applied self-organizing systems. Springer, 45--55.

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