mlsanityData to 14 Oct 2026

Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car

arXiv 2308.13088 ↗First posted 24 Aug 2023cs.RO cs.AI cs.LGcited by 1

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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.

Builds on

15 of 37 references.

References · 15

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  11. 2020Average-reward model-free reinforcement learning: a systematic review and literature mappingcited by 22
  12. 2019Model-Free Reinforcement Learning for Financial Portfolios: A Brief Surveycited by 10
3 more
  1. 2021Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Datacited by 10
  2. 2019Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulationscited by 6
  3. 2018Autonomous Driving System Design for Formula Student Driverless Racecarcited by 4

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