Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 1; pp. 11 - 32
• Main Authors: Haydari, Ammar, Yilmaz, Yasin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; autonomous driving; Autonomous vehicles; Control systems; Deep learning; Deep reinforcement learning; Intelligent transportation systems; Machine learning; Markov processes; multi-agent systems; Robotics; Traffic control; traffic signal control; Traffic signals; Traffic surveys; Transportation applications
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3008612

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.