From Local to Global: A Curriculum Learning Approach for Reinforcement Learning-based Traffic Signal Control

Nianzhao Zheng, Jialong Li, Zhenyu Mao, Kenji Tei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traffic signal control (TSC) is one of the effective ways to mitigate traffic congestion, a growing problem causing significant economic loss to urban areas. In recent years, there is an increasing interest in using reinforcement learning (RL) in TSC since RL shows great potential in optimizing control policy for complex real-world traffic conditions. However, one concerning problem is the huge computing time/resources required to train the control policy for large-scale TSC. To address this problem, this paper proposes a method that utilizes a curriculum to help speed up the training process. Control policies of different single-intersection maps are learned first and then referenced to a large-scale map consisting of a certain number of different intersections. The preliminary evaluation demonstrates that our method achieves a jump-start compared to that of learning the target task from scratch.

Original languageEnglish
Title of host publication2022 2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages253-258
Number of pages6
ISBN (Electronic)9781665482233
DOIs
Publication statusPublished - 2022
Event2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022 - Xiamen, China
Duration: 2022 Jun 102022 Jun 12

Publication series

Name2022 2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022

Conference

Conference2nd IEEE International Conference on Software Engineering and Artificial Intelligence, SEAI 2022
Country/TerritoryChina
CityXiamen
Period22/6/1022/6/12

Keywords

  • curriculum learning
  • reinforcement learning
  • traffic signal control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software
  • Control and Optimization

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