Deep reinforcement learning for traffic light optimization

Mustafa Coskun, Abdelkader Baggag, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Deep Reinforcement Learning has the potential of practically addressing one of the most pressing problems in road traffic management, namely that of traffic light optimization (TLO). The objective of the TLO problem is to set the timings (phase and duration) of traffic lights in order to minimize the overall travel time of the vehicles that traverse the road network. In this paper, we introduce a new reward function that is able to decrease travel time in a micro-simulator environment. More specifically, our reward function simultaneously takes the traffic flow and traffic delay into account in order to provide a solution to the TLO problem. We use both Deep Q-Learning and Policy Gradient approaches to solve the resulting reinforcement learning problem.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsHanghang Tong, Jeffrey Yu, Zhenhui Li, Feida Zhu
PublisherIEEE Computer Society
Pages564-571
Number of pages8
ISBN (Electronic)9781538692882
DOIs
StatePublished - Feb 7 2019
Externally publishedYes
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Country/TerritorySingapore
CitySingapore
Period11/17/1811/20/18

Keywords

  • Deep Learning
  • Traffic Light Optimization

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