Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car
This work addresses autonomous navigation for Formula Student competitions, but it is incremental as it builds on existing RL methods applied to a new domain.
The paper tackled autonomous control for a Formula Student race car by training deep reinforcement learning algorithms in simulation and transferring them to a real-world Turtlebot2 platform, successfully enabling racing in both environments.
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.