On learning racing policies with reinforcement learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911103166971904 |
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| author | Czechmanowski, Grzegorz Węgrzynowski, Jan Kicki, Piotr Walas, Krzysztof |
| author_facet | Czechmanowski, Grzegorz Węgrzynowski, Jan Kicki, Piotr Walas, Krzysztof |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02420 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On learning racing policies with reinforcement learning Czechmanowski, Grzegorz Węgrzynowski, Jan Kicki, Piotr Walas, Krzysztof Robotics Systems and Control 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. |
| title | On learning racing policies with reinforcement learning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2504.02420 |