Deep Reinforcement Learning for Local Path Following of an Autonomous
  Formula SAE Vehicle
Harvey Merton Thomas Delamore Karl Stol Henry Williams
With the continued introduction of driverless events to Formula:Society of
Automotive Engineers (F:SAE) competitions around the world, teams are
investigating all aspects of the autonomous vehicle stack. This paper presents
the use of Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning
(IRL) to map locally-observed cone positions to a desired steering angle for
race track following. Two state-of-the-art algorithms not previously tested in
this context: soft actor critic (SAC) and adversarial inverse reinforcement
learning (AIRL), are used to train models in a representative simulation. Three
novel reward functions for use by RL algorithms in an autonomous racing context
are also discussed. Tests performed in simulation and the real world suggest
that both algorithms can successfully train models for local path following.
Suggestions for future work are presented to allow these models to scale to a
full F:SAE vehicle.