Deep reinforcement learning for traffic signal control: A review
Document Type
Article
Publication Date
1-1-2020
Abstract
Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas worldwide. The integration of the newly emerging deep learning approach and the traditional reinforcement learning approach has created an advanced approach called deep reinforcement learning (DRL) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. This article presents a review of the attributes of traffic signal control (TSC), as well as DRL architectures and methods applied to TSC, which helps to understand how DRL has been applied to address traffic congestion and achieve performance enhancement. The review also covers simulation platforms, a complexity analysis, as well as guidelines and design considerations for the application of DRL to TSC. Finally, this article presents open issues and new research areas with the objective to spark new interest in this research field. To the best of our knowledge, this is the first review article that focuses on the application of DRL to TSC.
Keywords
Reinforcement learning, Deep learning, Neurons, Computational modeling, Analytical models, Complexity theory, Licenses, Artificial intelligence, deep learning, deep reinforcement learning, traffic signal control
Divisions
Computer
Funders
Novel Clustering algorithm based on Reinforcement Learning for the Optimization of Global and Local Network Performances in Mobile Networks - Malaysian Ministry of Education through Fundamental Research Grant Scheme (FRGS/1/2019/ICT03/SYUC/01/1),Sunway University (CR-UM-SST-DCIS-2018-01),Sunway University (RK004-2017),Universiti Malaya (CR-UM-SST-DCIS-2018-01),Universiti Malaya (RK004-2017)
Publication Title
IEEE Access
Volume
8
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA