In order to improve the joint control effect of multi-crossing, Multi-Agent Deep Recurrent Q-Network (MADRQN) for real-time control of multi-intersection traffic signals is proposed in this paper. Firstly, the traffic light control is modeled as a Markov decision process, wherein one controller at each crossing is considered as an agent. Secondly, agents are clustered according to their position and observation. Then, information sharing and centralized training are conducted within each cluster. Also the value function network parameters of agents with the highest critic value are shared with other agent at the end of every training process. The simulated experimental results under Simulation of Urban MObility (SUMO) show that the proposed method can reduce the amount of communication data, make information sharing of agents and centralized training more feasible and efficient. The average delay of vehicles is reduced obviously compared with the state-of-the-art traffic light control methods based on multi-agent deep reinforcement learning. The proposed method can effectively alleviate traffic congestion.
关键词
交通信号灯协同控制/集中训练分散执行/强化学习智能体聚类/生长型神经气/深度循环Q网络
Key words
Traffic light cooperative control/Centralized training with decentralized execution/Reinforcement learning agent cluster/Growing neural gas/Deep recurrent Q-network
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