Abstract:

With the rapid improvement of the national economy in recent years, people's travel demand has increased, bringing increasingly severe pressure on the current urban traffic signal system relying on traditional non-intelligent traffic lights. The significant increase in the complexity of the traffic network has led to the development of traffic signal control from a single-point problem to a system engineering problem, and the development of artificial intelligence technology brings more methods to dealing with urban traffic signal control. Swarm intelligence methods, represented by multi-agent reinforcement learning, have been widely used in traffic signal control and optimization, including traffic light control, autonomous driving, and vehicle-road collaboration. Compared to traditional methods, multi-agent reinforcement learning can empower the intelligence of traffic signal systems while implementing large-scale traffic signal system collaboration to improve the efficiency of urban traffic operations. The various components involved in urban transportation must collaborate in the vision of intelligent urban traffic. Multi-agent reinforcement learning is of great research value in urban traffic signal control and optimization. This paper will systematically introduce the basic theory of multi-agent deep reinforcement learning and its use in urban traffic signal optimization, summarize the existing approaches and analyze the drawbacks of each method. In addition, this paper will outline the challenges of multi-agent reinforcement learning methods for urban traffic signal optimization. Then the paper points out possible future research directions to promote the development of multi-agent reinforcement learning methods in urban traffic signal optimization.

Key words: multi-agent reinforcement learning, intelligent traffic, traffic signal control, autonomous driving