# Deep Reinforcement Learning for Autonomous Driving: A Survey

“Deep Reinforcement Learning for Autonomous Driving: A Survey” (2021), by Bangalore Ravi Kiran et al. Computer Science research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Bangalore Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Kumar Yogamani, Patrick Pérez
- Published: February 10, 2021
- Field: Computer Science
- DOI: https://doi.org/10.1109/tits.2021.3054625
- Free PDF: https://arxiv.org/pdf/2002.00444

## Abstract

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.

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