图 书 馆
使用线性规划的学徒学习Apprenticeship Learning Using Linear Programming |
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| 课程网址: | http://videolectures.net/icml08_syed_alu/ |
| 主讲教师: | Umar Syed |
| 开课单位: | 宾夕法尼亚大学 |
| 开课时间: | 2008-08-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in that the MDP's true reward function is assumed to be unknown. We show how to frame apprenticeship learning as a linear programming problem, and show that using an off-the-shelf LP solver to solve this problem results in a substantial improvement in running time over existing methods --- up to two orders of magnitude faster in our experiments. Additionally, our approach produces stationary policies, while all existing methods for apprenticeship learning output policies that are "mixed", i.e. randomized combinations of stationary policies. The technique used is general enough to convert any mixed policy to a stationary policy. |
| 关 键 词: | 学徒学习; 马尔可夫决策; 线性规划; 固定政策 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-07：lxf |
| 阅读次数: | 48 |