DuaLight: Enhancing Traffic Signal Control by Leveraging
  Scenario-Specific and Scenario-Shared Knowledge
Jiaming Lu Jingqing Ruan Haoyuan Jiang Ziyue Li Hangyu Mao Rui Zhao
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.