An Improved Traffic Signal Control Method Based on Multi-agent Reinforcement Learning

Area traffic signal control is important to alleviate urban traffic congestion. In this paper, we propose an improved multi-agent proximal policy optimization (MAPPO) algorithm via combine intrinsic curiosity module and proximal policy optimization to control area traffic signal. In the proposed algorithm, a multi-intersection traffic network is modeled as a multi-agent system and each agent is trained to search the optimal strategy. We validate our algorithm performance on the simulation of mobility (SUMO) platform. Experimental results show that the proposed algorithm can effectively reduce queue lengths and waiting time. Also, the performance of our algorithm is superior to MAPPO and fixed-time control.