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Effectiveness of Transfer Learning in Autonomous Driving using Model Car

We have known that reinforcement learning, deep learning, and deep reinforcement learning effectively acquire action rules for the autonomous motion of objects. However, it is known that these learning processes require a large amount of learning time. Besides, we should consider the similarity of the environment between the training target and the test target. In actual autonomous driving, there is no such thing as driving only on a course that has been learned in advance. In this study, the autonomous driving of a model car is used as the experimental object. The training target for acquiring the learning model and the actual driving courses are changed. In this study, we report on the effectiveness of transfer learning using a model car as the basis for a learning model acquired by reinforcement learning.