# A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2105.14218 · HTML: https://mlsanity.com/paper/2105.14218/

## Key facts

- Cited by 10 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 42 of its 69 references that are in the map.
- First posted on arXiv 29 May 2021.

## Placed in

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

## Builds on (most cited first, 30 of 42)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving](https://mlsanity.com/paper/1807.03776/) (2018)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [Virtual to Real Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1704.03952/) (2017)
- [Deep Imitative Models for Flexible Inference, Planning, and Control](https://mlsanity.com/paper/1810.06544/) (2018)
- [Safe Reinforcement Learning on Autonomous Vehicles](https://mlsanity.com/paper/1910.00399/) (2019)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1811.11329/) (2018)
- [Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning](https://mlsanity.com/paper/2002.02667/) (2020)
- [Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving](https://mlsanity.com/paper/1904.07189/) (2019)
- [Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints](https://mlsanity.com/paper/1904.00231/) (2019)
- [Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning](https://mlsanity.com/paper/1903.03642/) (2019)
- [A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning](https://mlsanity.com/paper/1707.02515/) (2017)

## Built on by (most cited first, 10 of 10)

- [Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking](https://mlsanity.com/paper/2205.06750/) (2022)
- [Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems](https://mlsanity.com/paper/2210.14991/) (2022)
- [Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea](https://mlsanity.com/paper/2402.08502/) (2024)
- [A Risk-Averse Preview-based Q-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles](https://mlsanity.com/paper/2112.03232/) (2021)
- [Robust AI Driving Strategy for Autonomous Vehicles](https://mlsanity.com/paper/2207.07829/) (2022)
- [Robot path planning using deep reinforcement learning](https://mlsanity.com/paper/2302.09120/) (2023)
- [Sim-to-Real Transfer of Adaptive Control Parameters for AUV Stabilization under Current Disturbance](https://mlsanity.com/paper/2310.11075/) (2023)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)

## Abstract (arXiv)

In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many existing contributions can be attributed to the pipeline approach, which consists of many hand-crafted modules, each with a functionality selected for the ease of human interpretation. However, this approach does not automatically guarantee maximal performance due to the lack of a system-level optimization. Therefore, this paper also presents a growing trend of work that falls into the end-to-end approach, which typically offers better performance and smaller system scales. However, their performance also suffers from the lack of expert data and generalization issues. Finally, the remaining challenges applying deep RL algorithms on autonomous driving are summarized, and future research directions are also presented to tackle these challenges.