Transfer Learning Method in Reinforcement Learning-based Traffic Signal Control

Zhenyu Mao, Jialong Li, Nianzhao Zheng, Kenji Tei, Shinichi Honiden

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

1 Citation (Scopus)

Abstract

Traffic signal control is becoming more important in intelligent transport systems. Existing studies managed to increase the traffic efficiency on the assumption of a stable traffic environment where no emergencies occur. However, accidents and road closures happen from time to time, and the existing studies cannot guarantee efficiency when such temporary changes happen in the road conditions. Thus, we designed a transfer learning method for existing reinforcement learning-based traffic signal control systems. Our proposed method uses parameters from the previous training model to initialize the new model to increase its initial performance, thus speeding up the learning process and reducing the time needed to adapt to road condition changes.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages304-307
Number of pages4
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: 2021 Oct 122021 Oct 15

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period21/10/1221/10/15

Keywords

  • intelligent transport systems
  • reinforcement learning
  • traffic signal control
  • transfer learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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