图 书 馆
不可知的KWIK学习和有效的近似强化学习Agnostic KWIK learning and efficient approximate reinforcement learning |
|
| 课程网址: | http://videolectures.net/colt2011_szepesvari_agnostic/ |
| 主讲教师: | Csaba Szepesvári |
| 开课单位: | 阿尔伯塔大学 |
| 开课时间: | 2011-08-02 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A popular approach in reinforcement learning is to use a model-based algorithm, i.e., an algorithm that utilizes a model learner to learn an approximate model to the environment. It has been shown such a model-based learner is efficient if the model learner is efficient in the so-called \knows what it knows" (KWIK) framework. A major limitation of the standard KWIK framework is that, by its very definition, it covers only the case when the (model) learner can represent the actual environment with no errors. In this paper, we introduce the agnostic KWIK learning model, where we relax this assumption by allowing nonzero approximation errors. We show that with the new de finition that an efficient model learner still leads to an effcient reinforcement learning algorithm. At the same time, though, we find that learning within the new framework can be substantially slower as compared to the standard framework, even in the case of simple learning problems. |
| 关 键 词: | 强化学习; 模型学习; KWIK框架; 学习算法; 学习环境 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2019-05-26：cwx |
| 阅读次数: | 55 |