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

• URL: http://arxiv.org/abs/2308.13088v1
• Date: Thu, 24 Aug 2023 21:16:03 GMT
• Title: Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car
• Authors: Aakaash Salvaji, Harry Taylor, David Valencia, Trevor Gee, Henry Williams
• Abstract summary: This paper presents the initial investigation into utilising Deep Reinforcement Learning (RL) for end-to-end control of an autonomous FS race car. 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.
• Score: 1.0124625066746598
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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