# Learning Autonomous Race Driving with Action Mapping Reinforcement Learning

arXiv: https://arxiv.org/abs/2406.14934 · HTML: https://mlsanity.com/paper/2406.14934/

## 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 24 of its 46 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [Learning-based Model Predictive Control for Safe Exploration](https://mlsanity.com/paper/1803.08287/) (2018)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving](https://mlsanity.com/paper/1810.12778/) (2018)
- [Learning How to Autonomously Race a Car: a Predictive Control Approach](https://mlsanity.com/paper/1901.08184/) (2019)
- [Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data](https://mlsanity.com/paper/2104.11106/) (2021)
- [Real-Time Control for Autonomous Racing Based on Viability Theory](https://mlsanity.com/paper/1701.08735/) (2017)
- [Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model](https://mlsanity.com/paper/1707.05303/) (2017)

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

- [Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning](https://mlsanity.com/paper/2608.00113/) (2026)

## Abstract (arXiv)

Autonomous race driving poses a complex control challenge as vehicles must be operated at the edge of their handling limits to reduce lap times while respecting physical and safety constraints. This paper presents a novel reinforcement learning (RL)-based approach, incorporating the action mapping (AM) mechanism to manage state-dependent input constraints arising from limited tire-road friction. A numerical approximation method is proposed to implement AM, addressing the complex dynamics associated with the friction constraints. The AM mechanism also allows the learned driving policy to be generalized to different friction conditions. Experimental results in our developed race simulator demonstrate that the proposed AM-RL approach achieves superior lap times and better success rates compared to the conventional RL-based approaches. The generalization capability of driving policy with AM is also validated in the experiments.