Joint Optimization of Traffic Signal Control and Vehicle Routing in
  Signalized Road Networks using Multi-Agent Deep Reinforcement Learning
Xianyue Peng Hang Gao Gengyue Han Hao Wang Michael Zhang
Urban traffic congestion is a critical predicament that plagues modern road
networks. To alleviate this issue and enhance traffic efficiency, traffic
signal control and vehicle routing have proven to be effective measures. In
this paper, we propose a joint optimization approach for traffic signal control
and vehicle routing in signalized road networks. The objective is to enhance
network performance by simultaneously controlling signal timings and route
choices using Multi-Agent Deep Reinforcement Learning (MADRL). Signal control
agents (SAs) are employed to establish signal timings at intersections, whereas
vehicle routing agents (RAs) are responsible for selecting vehicle routes. By
establishing relevance between agents and enabling them to share observations
and rewards, interaction and cooperation among agents are fostered, which
enhances individual training. The Multi-Agent Advantage Actor-Critic algorithm
is used to handle multi-agent environments, and Deep Neural Network (DNN)
structures are designed to facilitate the algorithm's convergence. Notably, our
work is the first to utilize MADRL in determining the optimal joint policy for
signal control and vehicle routing. Numerical experiments conducted on the
modified Sioux network demonstrate that our integration of signal control and
vehicle routing outperforms controlling signal timings or vehicles' routes
alone in enhancing traffic efficiency.