Zero-Shot Policy Transfer in Autonomous Racing:Reinforcement Learning vs Imitation Learning скачать в хорошем качестве

Zero-Shot Policy Transfer in Autonomous Racing:Reinforcement Learning vs Imitation Learning

In this work, we compare two leading methods for training neural network controllers, Reinforcement Learning and Imitation Learning, for the autonomous racing task. We compare their viability by analyzing their performance and safety when deployed in novel scenarios outside their training via zero-shot policy transfer. Our evaluation is made up of a large number of experiments in simulation and on our real-world hardware platform that analyze whether these algorithms remain effective when transferred to the real-world. Our results show reinforcement learning outperforming imitation learning in most scenarios. However, the increased performance comes at the cost of reduced safety. Thus, both methods are effective under different criteria.