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Multi-Agent Reinforcement Learning for Traffic Signal Control

thesis
posted on 2022-07-18, 00:57 authored by Chunliang WuChunliang Wu

Traffic signal control (TSC) is an essential and effective approach to reduce traffic delay. Reinforcement Learning (RL) provides a new way of designing TSC systems that allow agents to learn optimal control policy through interacting with the environment without models. However, developing an RL-based TSC algorithm for a large-scale network with practical constraints is still an open question. This thesis develops multi-agent RL algorithms with fast learning speed, strong robustness, and scalability for large-scale networks. This thesis provides insights for transport engineers to develop efficient, scalable, and robust RL methods for networked TSC systems in a real traffic environment.

History

Campus location

Australia

Principal supervisor

Zhenliang Ma

Additional supervisor 1

Inhi Kim

Additional supervisor 2

Zhiyuan Liu

Year of Award

2022

Department, School or Centre

Civil Engineering

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Engineering

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