Deep reinforcement learning in transportation research: A review

Transportation Research Interdisciplinary Perspectives

Tanvir Ahamed¹  Bo Zou²  Nahid Parvez Farazi³  Limon Barua³ 

[1] Department of Civil, Material, and Environmental Engineering, University of Illinois at Chicago, United States;Corresponding author.;Department of Civil, Material, and Environmental Engineering, University of Illinois at Chicago, United States;

关键词: Deep reinforcement learning;    Transportation research;    Application domain;    Literature review;    Synthetic discussion;   

DOI  :  

来源: DOAJ

【 摘 要 】

Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic review of existing DRL applications and adaptations in transportation research remains missing. The objective of this paper is to fill this gap. We expose the broad transportation research community to the methodological fundamentals of DRL, and present what have been accomplished in the literature by reviewing a total of 155 relevant papers that have appeared between 2016 and 2020. Based on the review, we further synthesize the applicability, strengths, shortcomings, issues, and directions for future DRL research in transportation, along with a discussion on the available DRL research resources. We hope that this review will serve as a useful reference for the transportation community to better understand DRL and its many potentials to advance research, and to stimulate further explorations in this exciting area.

【 授权许可】

Unknown