图 书 馆
使用强化学习调整时间约束问题的优化器Tuning Optimizers for Time-Constrained Problems using Reinforcement Learning |
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| 课程网址: | http://videolectures.net/opt08_ruvolo_toftc/ |
| 主讲教师: | Paul Ruvolo |
| 开课单位: | 圣地亚哥大学 |
| 开课时间: | 2008-12-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-based “controllers” that modulate the behavior of the op- timizer during the optimization process. For example, in the LMA a damping parameter λ is dynamically modified based on a set of rules that were developed using various heuristic arguments. Here we show that a modern reinforcement learning technique utilizing a very simple state space can dramatically improve the performance of general purpose optimizers, like the LMA, on problems where the number of function evaluations allowed is constrained by a budget. Results are given on both classical non-linear optimization problems as well as a difficult computer vision task. Interestingly the controllers learned for a particular opti- mization domain work well on other optimization domains. Thus, the controller appeared to have extracted optimization rules that were not just domain specific but generalized across a range of optimization domains. |
| 关 键 词: | 优化算法; 通用优化器; 规则动态 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-09：cjy |
| 阅读次数: | 60 |