James Jianqiao Yu
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Department of Computer Science

University of York

CSE/139, YO10 5GH, UK

jqyu(at)ieee.org CV Google Scholar
Attn-CommNet: Coordinated Traffic Lights Control on Large-scale Network Level

Jiashi Gao, Xinming Shi, and James J.Q. Yu*

Proc. IEEE International Conference on Tools with Artificial Intelligence, Washington, D.C., US, November 2021

Traffic lights control could be regarded as a multi-agent coordinated problem. A model-free reinforcement learning (RL) approach is a powerful framework for solving such coordinated policy-making problems without prior environmental knowledge. In order to approach a global policy, communication among agents needs to be built. To enable dynamic and scalable communication, we propose a new RL model, CommNet based on Local Attention Mechanism (Attn-CommNet), which uses local selection and attention mechanism between hidden layers to facilitate cooperation. We evaluated the proposed method using synthetic and real word traffic flows under multi-scale road networks. The results demonstrate that the proposed method can get better performance in multi-scale problems, especially large-scale problems compared to the state-of-the-art methods.