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Reinforcement learning has been revolutionizing the traditional trafficsignal control task, showing promising power to relieve congestion and improveefficiency. However, the existing methods lack effective learning mechanismscapable of absorbing dynamic information inherent to a specific scenario anduniversally applicable dynamic information across various scenarios. Moreover,within each specific scenario, they fail to fully capture the essentialempirical experiences about how to coordinate between neighboring and targetintersections, leading to sub-optimal system-wide outcomes.  Viewing these issues, we propose DuaLight, which aims to leverage both theexperiential information within a single scenario and the generalizableinformation across various scenarios for enhanced decision-making.Specifically, DuaLight introduces a scenario-specific experiential weightmodule with two learnable parts: Intersection-wise and Feature-wise, guidinghow to adaptively utilize neighbors and input features for each scenario, thusproviding a more fine-grained understanding of different intersections.Furthermore, we implement a scenario-shared Co-Train module to facilitate thelearning of generalizable dynamics information across different scenarios.Empirical results on both real-world and synthetic scenarios show DuaLightachieves competitive performance across various metrics, offering a promisingsolution to alleviate traffic congestion, with 3-7\% improvements. The code isavailable under: https://github.com/lujiaming-12138/DuaLight.

DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge