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.

翻译：随着无人驾驶赛事在全球Formula：Society of Automotive Engineers （F：SAE）竞赛中持续推广，各参赛团队正全面探索自动驾驶技术体系。本文提出利用深度强化学习（DRL）与逆强化学习（IRL）将局部观测到的锥桶位置映射为赛道跟踪所需的目标转向角。采用两种此前未在该场景下验证的最先进算法——柔性演员-评论家（SAC）与对抗式逆强化学习（AIRL），在代表性仿真环境中训练模型。同时讨论了三类面向无人竞速场景的新型奖励函数设计方案。仿真测试与实车实验表明，两种算法均能有效训练出适用于局部路径跟踪的模型。最后提出了未来研究方向，以支持这些模型扩展至完整的F：SAE赛车系统。