图 书 馆
一种随机最优控制的路径积分方法A path integral approach to stochastic optimal control |
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| 课程网址: | http://videolectures.net/oiml05_kappen_piaso/ |
| 主讲教师: | Bert Kappen |
| 开课单位: | 奈梅亨拉德布德大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many problems in machine learning use a probabilistic description. Examples are pattern recognition methods and graphical models. As a consequence of this uniform description, one can apply generic approximation methods such as mean field theory and sampling methods. Another important class of machine learning problems are the reinforcement learning problems, aka optimal control problems. Here, also a probabilistic description is used, but up to now efficient mean field approximations have not been obtained. In this presentation, I consider linear-quadratic control of an arbitrary dynamical system and show, that for this class of stochastic control problems the non-linear Hamilton-Jacobi-Bellman equation can be transformed into a linear equation. The transformation is similar to the transformation used to relate the Schrödinger equation to the Hamilton-Jacobi formalism. The computation can be performed efficiently by means of a forward diffusion process that can be computed by stochastic integration or that can be described by a path integral. For this path integral it is expected that a variational mean field approximation could be derived. |
| 关 键 词: | 机器学习; 概率描述; 最优控制问题; 线性方程; 路径积分 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：张荧(课程编辑志愿者) |
| 阅读次数: | 216 |