Autonomous racing with scaled race cars has gained increasing attention as an effective approach for developing perception, planning and control algorithms for safe autonomous driving at the limits of the vehicle's handling. To train agile control policies for autonomous racing, learning-based approaches largely utilize reinforcement learning, albeit with mixed results. In this study, we benchmark a variety of imitation learning policies for racing vehicles that are applied directly or for bootstrapping reinforcement learning both in simulation and on scaled real-world environments. We show that interactive imitation learning techniques outperform traditional imitation learning methods and can greatly improve the performance of reinforcement learning policies by bootstrapping thanks to its better sample efficiency. Our benchmarks provide a foundation for future research on autonomous racing using Imitation Learning and Reinforcement Learning.

翻译：缩比例赛车自主驾驶技术因能在车辆操控极限下开发安全自动驾驶所需的感知、规划与控制算法而日益受到关注。为训练自主赛车的敏捷控制策略，基于学习的方法主要采用强化学习，但结果参差不齐。本研究针对直接应用于赛车或用于引导强化学习的多种模仿学习策略，在仿真环境与缩比例真实环境两个尺度上进行了基准测试。结果表明，交互式模仿学习技术优于传统模仿学习方法，并能通过更优的样本效率有效提升强化学习策略的引导训练性能。本基准测试为未来基于模仿学习与强化学习的自主赛车研究奠定了坚实基础。