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

Reinforcement learning-based approach called DuaLight is proposed for traffic signal control to alleviate congestion and improve efficiency by leveraging scenario-specific experiential information and generalizable dynamics across scenarios, achieving competitive performance and offering 3-7% improvements in traffic congestion.

DuaLight：利用特定场景和共享场景知识优化交通信号控制