图 书 馆
基于层次模型的强化学习Hierarchical Model-Based Reinforcement Learning |
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| 课程网址: | http://videolectures.net/icml08_jong_hmb/ |
| 主讲教师: | Nicholas Jong |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying structure. Model-based algorithms, which provided the first finite-time convergence guarantees for reinforcement learning, may also play an important role in coping with the relative scarcity of data in large environments. In this paper, we introduce an algorithm that fully integrates modern hierarchical and model-learning methods in the standard reinforcement learning setting. Our algorithm, R-maxq, inherits the efficient model-based exploration of the R-max algorithm and the opportunities for abstraction provided by the MAXQ framework. We analyze the sample complexity of our algorithm, and our experiments in a standard simulation environment illustrate the advantages of combining hierarchies and models. |
| 关 键 词: | 相对稀缺性; 强化学习; 学习框架 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：yumf |
| 阅读次数: | 76 |