Single Intersection Traffic Light Control by Multi-agent Reinforcement Learning

Abstract

Deep reinforcement learning is a data-driven method, which is very promising for alleviating traffic congestion through intelligent control of traffic lights. In this paper, the traffic signal of an intersection is divided into four independent phases and then controlled by deep Q-network (DQN) models respectively. Models can receive observations from their own angle of view, i.e., north-south straight, north-south left turn, east-west straight, east-west left turn, instead of extracting features from the whole scene. We suppose that it is beneficial for learning better policy if agents could sense the environment more precisely. DQN models are jointly trained under the revised QMIX framework to promote coordination capability. For decentralized execution, traffic lights of the phase with the highest Q-value will turn green. The experiments are done under SUMO, the results demonstrate that our method obtains higher reward and lower delay compared to controlling the holistic cycle by using a single DQN model.