Racing Towards Reinforcement Learning based control of an Autonomous
  Formula SAE Car
Aakaash Salvaji Harry Taylor David Valencia Trevor Gee Henry Williams
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.