Application of Traffic Light Control in Oversaturated Urban Network Using Multi-Agent Deep Reinforcement Learning

Abstract

Adaptive traffic signal control techniques have been developed in numerous studies to increase traffic flow efficiency. Using traffic signals to design an adaptive traffic management system is ideal for reducing traffic congestion. Reinforcement learning is a branch of current approaches that try to learn a policy function through a trial-and-error process and maximize the reward through properly adjusted interaction with the learning agent's environment. We propose a traffic signal control architecture for an oversaturated urban network using Deep Q-Network. We have enhanced the learning process by incorporating diverse state information through upstream and downstream detailed traffic states. We conduct experiments on the Simulation of Urban MObility, an open-source traffic simulator that supports large-scale traffic signal control.