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:以规模型赛车为主的自动赛车已日益受到越来越多的注意,认为这是在车辆处理的限度内为安全自主驾驶制定认识、规划和控制算法的有效方法。为了对自动赛车的灵活控制政策进行培训,学习型方法主要利用强化学习,尽管其结果好坏参半。在这项研究中,我们为直接应用的或用于模拟和规模化现实世界环境中增压强化学习的机动车制定各种仿制学习政策的基准。我们表明,互动模仿学习技术优于传统的模仿学习方法,并且由于采样效率的提高,能够大大改进强化学习政策的绩效。我们的基准为今后利用模拟学习和强化学习研究自主赛提供了基础。