图 书 馆
无模型强化学习作为混合学习Model-Free Reinforcement Learning as Mixture Learning |
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| 课程网址: | http://videolectures.net/icml09_vlassis_mfr/ |
| 主讲教师: | Nikos Vlassis |
| 开课单位: | 克里特理工大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the in nite and nite horizon cases. We describe a Stochastic Approximation EM algorithm for likelihood maximization that, in the tabular case, is equivalent to a non-bootstrapping optimistic policy iteration algorithm like Sarsa(1) that can be applied both in MDPs and POMDPs. On the theoretical side, by relating the proposed stochastic EM algorithm to the family of optimistic policy iteration algorithms, we provide new tools that permit the design and analysis of algorithms in that family. On the practical side, preliminary experiments on a POMDP problem demonstrated encouraging results. |
| 关 键 词: | 自由强化学习; 抽样; 最大化概率 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-04-09：yumf |
| 阅读次数: | 127 |