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• W2566975477 abstract "The authors consider a controlled Markov chain whose transition probabilities and initial distribution are parameterized by an unknown parameter theta to some known parameter space Theta . There is a one-step reward associated with each pair of control and following state of the process. The objective is to maximize the expected value of the sum of one-step rewards over an infinite horizon. By introducing the loss associated with a control scheme, the authors show that the problem is equivalent to minimizing the loss. They define uniformly good adaptive control schemes and restrict attention to these schemes. They develop a lower bound on the loss associated with any uniformly good control scheme. Finally, they construct an adaptive control scheme whose loss equals the lower bound and is therefore optimal.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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• W2566975477 date "2003-01-06" @default.
• W2566975477 modified "2023-09-24" @default.
• W2566975477 title "Asymptotically efficient adaptive allocation schemes for controlled Markov chains: finite parameter space" @default.
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• W2566975477 doi "https://doi.org/10.1109/cdc.1988.194511" @default.
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