图 书 馆
使用基于样本的搜索进行高效的贝叶斯自适应强化学习Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search |
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| 课程网址: | http://videolectures.net/onlinelearning2012_guez_reinforcement_le... |
| 主讲教师: | Arthur Guez |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2013-05-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, finding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. In this talk we introduce a tractable, sampled-based method for approximate Bayes-opti mal planning which exploits Monte-Carlo tree search. Our approach outperformed prior Bayesian model-based RL algorithms by a significant margin on several well-known benchmark problems-- because it avoids expensive applications of Bayes rule within the search tree by lazily sampling models from the current beliefs. We illustrate the advantages of our approach by showing it working in an infinite state space domain which is qualitatively out of reach of almost all previous work in Bayesian exploration. |
| 关 键 词: | 贝叶斯模型; 强化学习; 优化规划 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-09：cjy |
| 阅读次数: | 38 |