On the adaptive control of a class of partially observed Markov decision processes |
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Authors: | Shun-Pin Hsu Ari Arapostathis |
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Institution: | a Department of Electrical Engineering, National Chung Hsing University, 250, Kuo-Kuang Rd., Taichung 402, Taiwan b Department of Electrical and Computer Engineering, The University of Texas at Austin, 1 University Station C0803, Austin, TX 78712-0240, USA |
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Abstract: | This paper is concerned with the adaptive control problem, over the infinite horizon, for partially observable Markov decision processes whose transition functions are parameterized by an unknown vector. We treat finite models and impose relatively mild assumptions on the transition function. Provided that a sequence of parameter estimates converging in probability to the true parameter value is available, we show that the certainty equivalence adaptive policy is optimal in the long-run average sense. |
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Keywords: | Adaptive control Markov decision processes Average-cost optimality |
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