CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
<|reference_start|>CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control: 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.<|reference_end|>
@article{ruan2024coslight:,
    title={CoSLight: Co-optimizing Collaborator Selection and Decision-making to
  Enhance Traffic Signal Control},
    author={Jingqing Ruan, Ziyue Li, Hua Wei, Haoyuan Jiang, Jiaming Lu, Xuantang
  Xiong, Hangyu Mao, Rui Zhao},
    journal={arXiv preprint arXiv:2405.17152},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17152},
    primaryClass={cs.MA cs.AI}
}