Interpretable Multi-Agent Reinforcement Learning for Traffic Signal Control: Influence Mechanism and Piecewise Linear Approximation

Traffic signal control plays a crucial role in intelligent transportation systems, with cooperative control being challenging to implement but essential for its effectiveness. Many methods model multi-intersection traffic networks as grids and address the problem using multi-agent reinforcement learning (RL). Despite these existing studies, there is an opportunity to further enhance our understanding of the connectivity and globality of the traffic networks by capturing the spatiotemporal traffic information with efficient neural networks in deep RL. In this paper, we propose a novel multi-agent actor-critic framework based on an interpretable influence mechanism with a centralized learning and decentralized execution method. Specifically, we first construct an actor-critic framework, for which the piecewise linear neural network (PWLNN), named biased ReLU (BReLU), is used as the function approximator to obtain a more accurate and theoretically grounded approximation, and exhibits interpretability. Then, to model the relationships among agents in multi-intersection scenarios, we introduce an interpretable influence mechanism based on efficient hinging hyperplanes neural network (EHHNN), which derives weights by analysis of variance (ANOVA) decomposition among agents and extracts spatiotemporal dependencies of the traffic features. Finally, our proposed framework is validated on two synthetic traffic networks and a real road network to coordinate signal control between intersections, achieving lower traffic delays across the entire traffic network compared with benchmark performance.