%0 Electronic Article %A I. Arel %A C. Liu %A T. Urbanik %A A.G. Kohls %K reinforcement learning-based multi-agent system %K network traffic signal control %K multiintersection vehicular networks %K local traffic statistics %K Q-Learning algorithm %K feedforward neural network %K longest-queue-first algorithm %K traffic signal scheduling %X 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. %@ 1751-956X %T Reinforcement learning-based multi-agent system for network traffic signal control %B IET Intelligent Transport Systems %D June 2010 %V 4 %N 2 %P 128-135 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=733omqhqkcc80.x-iet-live-01content/journals/10.1049/iet-its.2009.0070 %G EN