CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909227695472640 |
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| author | Ruan, Jingqing Li, Ziyue Wei, Hua Jiang, Haoyuan Lu, Jiaming Xiong, Xuantang Mao, Hangyu Zhao, Rui |
| author_facet | Ruan, Jingqing Li, Ziyue Wei, Hua Jiang, Haoyuan Lu, Jiaming Xiong, Xuantang Mao, Hangyu Zhao, Rui |
| contents | Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. The code is available at https://github.com/bonaldli/CoSLight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control Ruan, Jingqing Li, Ziyue Wei, Hua Jiang, Haoyuan Lu, Jiaming Xiong, Xuantang Mao, Hangyu Zhao, Rui Multiagent Systems Artificial Intelligence Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. The code is available at https://github.com/bonaldli/CoSLight. |
| title | CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2405.17152 |