Deep Reinforcement Learning for Autonomous Driving: A Survey
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.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Kiran, B Ravi
- Sobh, Ibrahim
- Talpaert, Victor
- Mannion, Patrick
- Al Sallab, Ahmad A
- Yogamani, Senthil
- Pérez, Patrick
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 4909-4926
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 6
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Artificial intelligence; Autonomous vehicles; Decision making; Mobile robots; Sensors; Surveys; Task analysis
- Subject Areas: Data and Information Technology; Highways;
Filing Info
- Accession Number: 01852052
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 21 2022 11:30AM