Dynamic traffic signal control based on multi-agent curricular transfer learning

This paper considers the smart city traffic signal control problem. The application of reinforcement learning in smart city traffic signal control has always been a hot research field. However, agents cannot learn good policies in complex environments with a large number of agents. Therefore, this paper proposes a course learning method with increasing number of agents in a hybrid environment, completes multi-agent course transfer learning based on MADDPG algorithm, and applies it to the field of traffic lights in smart city. Experimental results show that the performance of the proposed system is better than the widely used traffic signal control algorithms in large-scale intersection environment.