  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.
