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Optimal Adaptive Policies for Markov Decision Processes

Apostolos N. Burnetas and Michael N. Katehakis
Mathematics of Operations Research
Vol. 22, No. 1 (Feb., 1997), pp. 222-255
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/3690147
Page Count: 34
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Optimal Adaptive Policies for Markov Decision Processes
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Abstract

In this paper we consider the problem of adaptive control for Markov Decision Processes. We give the explicit form for a class of adaptive policies that possess optimal increase rate properties for the total expected finite horizon reward, under sufficient assumptions of finite state-action spaces and irreducibility of the transition law. A main feature of the proposed policies is that the choice of actions, at each state and time period, is based on indices that are inflations of the right-hand side of the estimated average reward optimality equations.

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