A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control

Traffic congestion is a major source of discomfort and economic losses in urban environments. Recently, the proliferation of traffic detectors and the advances in algorithms to efficiently process data have enabled taking a data-driven approach to mitigate congestion. In this context, this work proposes a reinforcement learning (RL) based distributed control scheme that exploits cooperation among intersections. Specifically, a RL controller is synthesized, which manipulates traffic signals using information from neighboring intersections in the form of an embedding obtained from a traffic prediction application. Simulation results using SUMO show that the proposed scheme outperforms classical techniques in terms of waiting time and other key performance indices.