0


线性可解MDPs理论的推广

Generalizations of the Theory of Linearly Solvable MDPs
课程网址: http://videolectures.net/cyberstat2012_dvijotham_linearly_solvabl...  
主讲教师: Krishnamurthy Dvijotham
开课单位: 华盛顿大学
开课时间: 2012-10-16
课程语种: 英语
中文简介:
我们提出了线性可解的最优控制理论的各种概括。一个概括是游戏理论和风险敏感设置的扩展,这需要用Renyi分歧取代KL成本。另一个扩展是有效的策略梯度算法的推导,其仅需要对各种问题公式(有限地平线,无限地平线,第一出口,打折)采样状态空间(而不是状态动作空间)。对于有限时空情况,我们表明PI ^ 2算法(路径积分政策改进)可以被视为我们的政策梯度算法应用于风险寻求目标时的一个特例。最后,我们将这些策略梯度算法的应用呈现给运动控制和动力系统中的各种问题。
课程简介: We present various generalizations of the theory of linearly solvable optimal control. One generalization is the extension to the game theoretic and risk-sensitive setting, which requires replacing KL costs with Renyi divergences. Another extension is the derivation of efficient policy gradient algorithms that only need one to sample the state space (rather than the state-action space) for various problem formulations (finite horizon, infinite horizon, first-exit, discounted). For the finite horizon case, we show that the PI^2 algorithm (Policy Improvement with Path Integrals) can be seen as a special case of our policy gradient algorithms when applied to a risk-seeking objective. Finally, we present applications of these policy gradient algorithms to various problems in movement control and power systems.
关 键 词: 线性可解; 最优控制理论; 策略梯度算法; 游戏理论; 风险敏感设置
课程来源: 视频讲座网
最后编审: 2020-10-26:zyk
阅读次数: 84