模型车比赛中自动驾驶迁移学习的基础研究

Shohei Chiba, Hisayuki Sasaoka
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引用次数: 7

摘要

强化学习、深度学习和深度强化学习可以有效获取物体自主运动的动作规则。然而,一些研究人员报告说,机器学习过程需要大量的学习时间。此外,该过程还需要考虑训练目标与测试目标之间环境的相似性。在实际的自动驾驶中,不存在只在事先学习过的课程上驾驶的说法。在这项研究中,我们使用了一种模型汽车自动驾驶的迁移学习算法。学习模式的获取与实际驾驶课程的培训目标发生了变化。在这项研究中,我们报告了迁移学习的有效性,使用模型汽车作为强化学习获得的学习模型的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Basic Study for Transfer Learning for Autonomous Driving in Car Race of Model Car
Reinforcement learning, deep learning, and deep reinforcement learning can effectively acquire action rules for the autonomous motion of objects. However, some researchers have reported that the machine learning process requires a large amount of learning time. Besides, the process needs to consider the similarity of the environment between the training target and the test target. There is no such thing as driving only on a course learned in advance in actual autonomous driving. In this study, we have used a transfer learning algorithm for autonomous drivings for model cars. 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.
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