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.

翻译：随着自主导航研究日益普及，大学生方程式（FS）赛事在其项目列表中新增了无人驾驶车辆（DV）类别。本文初步探讨了利用深度强化学习（RL）实现自主FS赛车端到端控制以应对这些竞赛的方法。我们在仿真环境中，基于Turtlebot2平台，在与全尺寸设计相似的赛道上训练了两种最先进的强化学习算法。结果表明，该方法能够成功学习在仿真环境中进行赛车驾驶，并迁移至实体平台的真实赛道。最后，我们分析了该方法的局限性，并为将强化学习应用于全尺寸自主FS赛车竞赛的未来方向提供指导。