Cooperative Multi-Agent Reinforcement Learning Framework for Edge Intelligence Empowered Traffic Light Control

Edge Intelligence (EI) technologies obtain an advance with promotion by Consumer Electronics (CE) and spread to the Intelligent Transportation System (ITS). As part of the edge in ITS, traffic lights suffer from overlooking the importance of cooperation among traffic lights and lack of long sequence scheduling. To address this challenge, we formulate the control problem of multi-intersection traffic lights as a multi-agent Markov game problem. In response, we propose a Cooperative Adaptive Control Method (CACOM), a framework based on multi-agent reinforcement learning. CACOM integrates the mixing network and the options framework. Specifically, the mixing network enables cooperation among intersections, and the options framework provides the ability for intersections to make a long sequence scheduling. Besides, we designed a weight generator for the mixing network based on the traffic conditions at intersections, allowing the agents to adjust their weights adaptively during cooperation. Finally, we build a simulator including two real-world urban road networks for extensive evaluation. In contrast to the best baseline methods, our approach achieves an average waiting time reduction of around 24% and 42% for high-priority vehicles in two scenarios. Moreover, the waiting time for all vehicles is decreased by approximately 15% and 6%, respectively.