Rasheed, Faizan and Yau, Kok-Lim Alvin and Md. Noor, Rafidah and Wu, Celimuge and Low, Yeh-Ching (2020) Deep reinforcement learning for traffic signal control: A review. IEEE Access, 8. pp. 208016-208044. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2020.3034141.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| 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) |
| Uncontrolled Keywords: | Reinforcement learning; Deep learning; Neurons; Computational modeling; Analytical models; Complexity theory; Licenses; Artificial intelligence; deep learning; deep reinforcement learning; traffic signal control |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 17 May 2023 06:47 |
| Last Modified: | 17 May 2023 06:47 |
| URI: | http://eprints.um.edu.my/id/eprint/37154 |
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