Deep reinforcement learning in transportation research: A review
Nahid Parvez Farazi, Bo Zou, Tanvir Ahamed, Limon Barua
Abstract
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.