Design and realization of a new architecture based on multi-agent systems and reinforcement learning for traffic signal control

Abstract

Increasing the number of cars in cities creates traffic congestion. This is due to static management of traffic lights. Reinforcement Learning RL algorithm is an artificial intelligence approach that enables adaptive real-time control at intersections. In this research paper, we purpose a new architecture based on multi-agent systems and RL algorithm in order to make the signal control system more autonomous, able to learn from its environment and make decisions to optimize road traffic.