On learning racing policies with reinforcement learning
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
Fully autonomous vehicles promise enhanced safety and efficiency. However, ensuring reliable operation in challenging corner cases requires control algorithms capable of performing at the vehicle limits. We address this requirement by considering the task of autonomous racing and propose solving it by learning a racing policy using Reinforcement Learning (RL). Our approach leverages domain randomization, actuator dynamics modeling, and policy architecture design to enable reliable and safe zero-shot deployment on a real platform. Evaluated on the F1TENTH race car, our RL policy not only surpasses a state-of-the-art Model Predictive Control (MPC), but, to the best of our knowledge, also represents the first instance of an RL policy outperforming expert human drivers in RC racing. This work identifies the key factors driving this performance improvement, providing critical insights for the design of robust RL-based control strategies for autonomous vehicles.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 34 references that are in the map. First posted on arXiv 3 Apr 2025.
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19 of 34 references.
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Other references · 18
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
- 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
- 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
- 2019acados: a modular open-source framework for fast embedded optimal controlcited by 68
- 2020Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learningcited by 65
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 64
- 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 50
- 2021Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learningcited by 45
- 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 40
- 2021NeuroBEM: Hybrid Aerodynamic Quadrotor Modelcited by 37
6 more
- 2019AMZ Driverless: The Full Autonomous Racing Systemcited by 29
- 2020Learning from Simulation, Racing in Realitycited by 16
- 2024MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraintscited by 7
- 2025RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platformscited by 4
- 2021A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Drivercited by 3
- 2024Learning dynamics models for velocity estimation in autonomous racingcited by 1
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Citing papers per year: 2025: 1.
1 paper.
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- 2025Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing
- 2022TC-Driver: Trajectory Conditioned Driving for Robust Autonomous Racing -- A Reinforcement Learning Approach
- 2024Learning Autonomous Race Driving with Action Mapping Reinforcement Learning
- 2024RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
- 2026Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
- 2024Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
- 2023Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing
- 2022A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing