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/AnonymousAccountss/CoSLight.

翻译：有效的多路口协作对于基于强化学习的交通信号控制缓解交通拥堵至关重要。现有工作主要选择相邻路口作为合作者。然而，相当一部分拥堵，甚至某些大范围拥堵，是由非相邻路口未能协作所导致的。为解决这些问题，我们提出将合作者选择分离为待学习的第二策略，与原始信号控制策略同步更新。具体而言，选择策略根据相位级和路口级特征实时自适应地选择最佳协作伙伴。在合成数据集和真实数据集上的实证结果有力地验证了我们方法的优越性，相比现有最先进方法实现了显著提升。代码可在 https://github.com/AnonymousAccountss/CoSLight 获取。