图 书 馆
一种高效强化学习的面向对象表示方法An Object-Oriented Representation for Efficient Reinforcement Learning |
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| 课程网址: | http://videolectures.net/icml08_diuk_oor/ |
| 主讲教师: | Carlos Diuk |
| 开课单位: | 新泽西州立大学 |
| 开课时间: | 2008-08-04 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object-Oriented MDPs (OO-MDPs), a representation based on objects and their interactions, which is a natural way of modeling environments and offers important generalization opportunities. We introduce a learning algorithm for deterministic OO-MDPs, and prove a polynomial bound in its sample complexity. We illustrate the performance gains of our representation and algorithm in the well-known Taxi domain, plus a real-life videogame. |
| 关 键 词: | 强化学习; 面向对象; 学习算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-18：cwx |
| 阅读次数: | 100 |