DDPGAT: Integrating MADDPG and GAT for optimized urban traffic light control

Abstract

Urban traffic control is a complex and dynamic multi-agent challenge, characterized by the need for efficient coordination and real-time responsiveness in fluctuating traffic conditions. Traditional methods often fall short in adapting to these dynamic environments. This article introduces “DDPGAT”, a novel framework that merges Multi-Agent Deep Deterministic Policy Gradients (MADDPG) with Graph Attention Networks (GATs) for optimized urban traffic control, further enhanced by a unique moral reward component. DDPGAT empowers traffic signal controllers as independent agents using GATs for dynamic road importance assessment. Shared attention scores during training enhance each agent's understanding of local and wider traffic patterns, essential for developing adaptive control policies. A key innovation in DDPGAT is the moral reward function, encouraging decisions that consider neighboring intersections' traffic, thus promoting ethical traffic management. The experiments demonstrate that DDPGAT significantly boosts traffic throughput and reduces congestion, confirming its effectiveness in diverse traffic conditions. The integration of MADDPG, GATs, and a moral reward strategy in DDPGAT presents a sophisticated, robust approach for managing the complexities of urban traffic control, marking a notable progression in intelligent traffic system technologies.