# Review of Deep Reinforcement Learning for Autonomous Driving

arXiv: https://arxiv.org/abs/2302.06370 · HTML: https://mlsanity.com/paper/2302.06370/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 10 of 10)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/1906.00208/) (2019)
- [Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges](https://mlsanity.com/paper/1707.02432/) (2017)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Exploring applications of deep reinforcement learning for real-world autonomous driving systems](https://mlsanity.com/paper/1901.01536/) (2019)

## Built on by (most cited first, 1 of 1)

- [On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations](https://mlsanity.com/paper/2503.22575/) (2025)

## Abstract (arXiv)

Since deep neural networks' resurgence, reinforcement learning has gradually strengthened and surpassed humans in many conventional games. However, it is not easy to copy these accomplishments to autonomous driving because state spaces are immensely complicated in the real world and action spaces are continuous and fine control is necessary. Besides, autonomous driving systems must also maintain their functionality regardless of the environment's complexity. The deep reinforcement learning domain (DRL) has become a robust learning framework to handle complex policies in high dimensional surroundings with deep representation learning. This research outlines deep, reinforcement learning algorithms (DRL). It presents a nomenclature of autonomous driving in which DRL techniques have been used, thus discussing important computational issues in evaluating autonomous driving agents in the real environment. Instead, it involves similar but not standard RL techniques, adjoining fields such as emulation of actions, modelling imitation, inverse reinforcement learning. The simulators' role in training agents is addressed, as are the methods for validating, checking and robustness of existing RL solutions.