  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.
