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强化学习，学徒学习和机器人控制Reinforcement Learning, Apprenticeship Learning and Robotic Control |
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| 课程网址: | http://videolectures.net/icml09_ng_itrlrc/ |
| 主讲教师: | Andrew Ng |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Reinforcement learning has proved to be a powerful method for robotic control. In this talk, drawing on examples from autonomous helicopter flight, quadruped robot control and autonomous driving, I'll describe some of the challenges we've faced in applying RL algorithms to various control problems, such as (i) Problems where the reward function is exceedingly difficult to specify by hand, and must itself be learned, (ii) Safe exploration, where one wishes to explore without damaging the robot, and (iii) Learning high performance controllers even if we have only an extremely inaccurate model of our robot's dynamics. Using apprenticeship learning - in which we learn by watching an expert demonstration - as a unifying theme, I'll also describe a few algorithms for addressing these challenges. |
| 关 键 词: | 机器人控制; 强化学习; 算法应用 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-24：cwx |
| 阅读次数: | 55 |