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.

使用深度强化学习（DRL）和逆强化学习（IRL）将局部观察到的锥体位置映射到期望的转向角度以进行赛道跟踪。两种先进算法，软演员批评（SAC）和对抗逆强化学习（AIRL），在代表性模拟中训练模型。在仿真和现实世界中进行的测试表明，这两种算法都可以成功训练用于局部路径跟踪的模型。提出了未来工作的建议，以使这些模型能够适用于完整的 Formula:SAE 车辆。