Explicit Coordination Based Multi-agent Reinforcement Learning for Intelligent Traffic Signal Control

Traffic signal control plays an important role in reducing urban traffic congestion. In complex traffic scenarios, coordinating phase signal control between intersections is a significant challenge. Reinforcement learning is widely used in the field of intelligent traffic signal control because it is good at dealing with sequence decision problems. The current reinforcement learning based approach makes phase decisions through coordinated cooperation. However, existing methods have difficulty with information exchange, because they lack semantic interpretation and explicit quantification of collaborative impact, which results in inefficient or conflicting phase coordination between intersections. Moreover, during the early exploration stage of reinforcement learning training, the phase output of the decision network is unreliable, making it difficult for the model to utilize decision information for self-supervised training. To address these issues, this paper proposes a self-supervised, explicit coordination based multi-agent reinforcement learning approach. Additionally, a phase boosting learning from demonstration method is introduced in the early training stages. Extensive experimental results demonstrate that this method can enhance collaboration among agents, outperforming baseline methods across multiple real-world traffic datasets, while also improving training stability and convergence speed.