NDLI: Optimality of pure strategies in stochastic decision processes

Content Provider	IEEE Xplore Digital Library
Author	Feinberg, E.A.
Copyright Year	1990
Description	Author affiliation: Dept. of Appl. Math. & Stat., State Univ. of New York, Stony Brook, NY, USA (Feinberg, E.A.)
Abstract	Discrete-time, infinite-horizon stochastic decision processes with various reward criteria are addressed. Sufficient conditions are obtained for the value of a class of strategies to be equal to the value of the subclass of nonrandomized strategies from this class. Two different methods for proving that nonrandomized strategies are as good as arbitrary ones are considered. The first method is based on the fact that a strategic measure for any strategy may be represented as a linear combination (or a linear operator) of strategic measures generated by nonrandomized strategies and the same initial distribution. This method is applicable to various criteria and classes of strategies. The second method is applicable to Markov decision processes with the expected total reward criterion. It is based on linearity properties of optimality equations, on the approximation of dynamic programming models by negative dynamic programming models, and on the replacement of the initial model by another one whose states represent information about the past in the initial model.<>
Starting Page	2149
Ending Page	2154
File Size	547358
Page Count	6
File Format	PDF
DOI	10.1109/CDC.1990.204006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-12-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Dynamic programming Strategic planning Mathematics Statistics Infinite horizon Sufficient conditions History Integral equations Linearity
Content Type	Text
Resource Type	Article

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