图 书 馆
近似线性规划中的约束松弛Constraint Relaxation in Approximate Linear Programs |
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| 课程网址: | http://videolectures.net/icml09_petrik_cral/ |
| 主讲教师: | Marek Petrik |
| 开课单位: | 马萨诸塞大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Approximate linear programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for the poor quality of ALP solutions in problems where the approximation induces virtual loops. We then introduce two methods for improving solution quality. One method rolls out selected constraints of the ALP, guided by the dual information. The second method is a relaxation of the ALP, based on external penalty methods. The latter method is applicable in domains in which rolling out constraints is impractical. Both approaches show promising empirical results for simple benchmark problems as well as for a more realistic blood inventory management problem. |
| 关 键 词: | 近似线性规划; 强化学习; 解决方案 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-13：yumf |
| 阅读次数: | 81 |