A Graph Attention Mechanism Based Multi-Agent Reinforcement Learning Method for Efficient Traffic Light Control

Traffic light control is vital for the efficiency of urban transportation. Recently, the increasing of vehicles has brought great challenges to the traffic light control system. However, traditional traffic light controlling methods are inefficient due to the sophistications of traffic dynamics. In this paper, we propose a Graph Attention mechanism based Multi-Agent Reinforcement Learning method (GA-MARL) by extending the Actor-Critic framework to improve the efficiency of cooperation in traffic signal control. The proposed algorithm is based on hard-attention and soft-attention mechanism, which can help agent filter information effectively and calculate the importance of other agents. In addition, we complete our algorithm by adopting the framework of Centralized Training with Decentralized Execution (CTDE) to overcome the challenge of non-stationary non-Markovian environments. Simulation results prove that our proposed method outperforms the representative methods in the literature.