Graph Attention Network-Based Multi-Agent Reinforcement Learning for Traffic Signal Priority Control

Reinforcement learning (RL) is widely applied to the problem of adaptive traffic signal control (ATSC). This paper presents an efficient RL algorithm to address ATSC issues. We adopt the decentralized multi-agent advantage actor-critic (A2C) algorithm, where distributed control methods introduce new challenges: the environment becomes partially observable for each local agent. To tackle this issue, we incorporate spatial infor-mation from neighboring agents using graph attention networks (GAT) to learn collaborative control strategies. Additionally, to better adapt to real-world road environments, we propose a hybrid reward function model. This model ensures the traffic efficiency for social vehicles while emphasizing the priority of emergency vehicles (EMV), thereby reducing their travel time. We numerically evaluate the proposed method in a simulated environment containing 25 intersections. Experimental results demonstrate that the approach presented in this paper has significant advantages compared to similar algorithms applied to multi-agent ATSC.