Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car
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Abstract (arXiv)
With the rising popularity of autonomous navigation research, Formula Student (FS) events are introducing a Driverless Vehicle (DV) category to their event list. This paper presents the initial investigation into utilising Deep Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car for these competitions. We train two state-of-the-art RL algorithms in simulation on tracks analogous to the full-scale design on a Turtlebot2 platform. The results demonstrate that our approach can successfully learn to race in simulation and then transfer to a real-world racetrack on the physical platform. Finally, we provide insights into the limitations of the presented approach and guidance into the future directions for applying RL toward full-scale autonomous FS racing.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 24 Aug 2023.
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15 of 37 references.
References · 15
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