Reinforcement learning-based multi-agent system for network traffic signal control

TLDR

Scheduling traffic signals across multiple intersections is a challenging AI problem in vehicular networks. This study proposes a multi‑agent reinforcement learning framework to derive an efficient traffic signal control policy that reduces average delay, congestion, and cross‑blocking. In a five‑intersection testbed, each intersection is managed by an autonomous agent; a central agent learns a Q‑learning value function with a neural network while outbound agents use a longest‑queue‑first algorithm and supply local traffic statistics. Experiments show that the multi‑agent RL controller outperforms isolated LQF control, demonstrating the potential for efficient distributed traffic signal management.

Abstract

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cross-blocking. A five-intersection traffic network has been studied in which each intersection is governed by an autonomous intelligent agent. Two types of agents, a central agent and an outbound agent, were employed. The outbound agents schedule traffic signals by following the longest-queue-first (LQF) algorithm, which has been proved to guarantee stability and fairness, and collaborate with the central agent by providing it local traffic statistics. The central agent learns a value function driven by its local and neighbours' traffic conditions. The novel methodology proposed here utilises the Q-Learning algorithm with a feedforward neural network for value function approximation. Experimental results clearly demonstrate the advantages of multi-agent RL-based control over LQF governed isolated single-intersection control, thus paving the way for efficient distributed traffic signal control in complex settings.