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线性bellman方程:理论与应用

Linear Bellman Equations: Theory and Applications
课程网址: http://videolectures.net/nipsworkshops09_todorov_lbeta/  
主讲教师: Emanuel Todorov
开课单位: 华盛顿大学
开课时间: 2010-01-19
课程语种: 英语
中文简介:
我将简要概述我们小组以及Bert Kappen小组最近开发的一类随机最优控制问题。该问题类非常通用,但具有许多独特的性质,包括指数变换(Hamilton Jacobi)Bellman方程的线性,贝叶斯推断的对偶性,逆最优控制问题的凸性,最优控制律的组合性,路径积分表示指数变换值函数的然后,我将重点介绍利用Bellman方程线性度的函数逼近方法,并说明这些方法如何扩展到高维连续动力系统。通过求解大但稀疏的线性问题,可以非常有效地计算一组固定基函数的权重。这使我们能够与数以亿计的(本地化)基地合作。尽管如此,高维状态空间的体积太大而无法填充局部基础,迫使我们考虑用于定位和整形这些基础的自适应方法。将比较几种这样的方法。
课程简介: I will provide a brief overview of a class stochastic optimal control problems recently developed by our group as well as by Bert Kappen's group. This problem class is quite general and yet has a number of unique properties, including linearity of the exponentially-transformed (Hamilton-Jacobi) Bellman equation, duality with Bayesian inference, convexity of the inverse optimal control problem, compositionality of optimal control laws, path-integral representation of the exponentially-transformed value function. I will then focus on function approximation methods that exploit the linearity of the Bellman equation, and illustrate how such methods scale to high-dimensional continuous dynamical systems. Computing the weights for a fixed set of basis functions can be done very efficiently by solving a large but sparse linear problem. This enables us to work with hundreds of millions of (localized) bases. Still, the volume of a high-dimensional state space is too large to be filled with localized bases, forcing us to consider adaptive methods for positioning and shaping those bases. Several such methods will be compared.
关 键 词: 控制问题; 指数变换; 贝叶斯
课程来源: 视频讲座网
最后编审: 2019-09-07:lxf
阅读次数: 54