Approximate Dynamic Programming for Optimal Stationary Control with   Control-Dependent Noise

doi:10.1109/TNN.2011.2165729

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	Approximate Dynamic Programming for Optimal Stationary Control with Control-Dependent Noise
	Jiang, Yu ; Jiang, Zhong-Ping
刊名	ieee神经网络汇刊
	2011
关键词	Approximate dynamic programming control-dependent noise optimal stationary control stochastic systems
DOI	10.1109/TNN.2011.2165729
英文摘要	This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Ito calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.; Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic; SCI(E); EI; 6; ARTICLE; 12; 2392-2398; 22
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/401232]
专题	工学院
推荐引用方式 GB/T 7714	Jiang, Yu,Jiang, Zhong-Ping. Approximate Dynamic Programming for Optimal Stationary Control with Control-Dependent Noise[J]. ieee神经网络汇刊,2011.
APA	Jiang, Yu,&Jiang, Zhong-Ping.(2011).Approximate Dynamic Programming for Optimal Stationary Control with Control-Dependent Noise.ieee神经网络汇刊.
MLA	Jiang, Yu,et al."Approximate Dynamic Programming for Optimal Stationary Control with Control-Dependent Noise".ieee神经网络汇刊 (2011).