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 taxon...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-06, Vol.23 (6), p.4909-4926
Main Authors: Kiran, B Ravi, Sobh, Ibrahim, Talpaert, Victor, Mannion, Patrick, Sallab, Ahmad A. Al, Yogamani, Senthil, Perez, Patrick
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3054625