| Approximate Dynamic Programming for Optimal Stationary Control with Control-Dependent Noise. | |
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MedLine Citation:
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PMID: 21954203 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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 Itô 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. |
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Authors:
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Yu Jiang; Zhong-Ping Jiang |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-9-26 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: - ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2011 Sep |
Date Detail:
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Created Date: 2011-9-28 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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