Deep Reinforcement Learning for Autonomous Driving: A Survey

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 6; pp. 4909 - 4926
• Main Authors: Kiran, B Ravi, Sobh, Ibrahim, Talpaert, Victor, Mannion, Patrick, Sallab, Ahmad A. Al, Yogamani, Senthil, Perez, Patrick
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-06-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; autonomous driving; Autonomous vehicles; Cloning; controller learning; Decision making; Deep learning; Deep reinforcement learning; Domains; imitation learning; inverse reinforcement learning; Machine learning; motion planning; Pipelines; Planning; Reinforcement learning; Robot sensing systems; safe reinforcement learning; Simulators; Task analysis; Taxonomy; trajectory optimisation
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2021.3054625

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.