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Open AccessJournal ArticleDOI

A modified dynamic programming method for markovian decision problems

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TLDR
In this article, a modified dynamic programming method for the problem of choosing the action at the beginning of each period which will maximize future total discounted income is described, and the convergence appears to be quite rapid.
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This article is published in Journal of Mathematical Analysis and Applications.The article was published on 1966-04-01 and is currently open access. It has received 147 citations till now. The article focuses on the topics: Monotone polygon & Decision problem.

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Book ChapterDOI

An extrapolation method for Bayesian control of Markov chains

TL;DR: In this paper, the authors consider a system with state space S and action space A(s), where state n+leS at stage n+1, ne IN :={1,2,...} is partially determined by the outcome of an i.i.d. random variable Xn not controllable by the decision maker.
Book ChapterDOI

Stochastische Produktionsglättungsmodelle bei stationärer und nichtstationärer Nachfrage

D. Bartmann
TL;DR: In this article, a Produktionsglattungsmodell is proposed, in which a Gut hergestellt wird einen glatteren Verlauf annehmen als der Absatz.
Journal ArticleDOI

Variational characterizations in Markov decision processes

TL;DR: In this paper, the authors derived bounds and variational characterizations for the solutions of variational Markov decision processes, and used them to measure the deviation of the current solution from optimality.
Journal Article

Identification of optimal policies in markov decision processes

Karel Sladký
- 01 Jan 2010 - 
TL;DR: A unified approach to value iteration algorithms that enables to generate lower and upper bounds on optimal values, as well as on the current policy, is presented.
Journal ArticleDOI

Bounds and elimination in generalized markov decisions

TL;DR: This paper extends these procedures to the generalized Markov decision process and forfeits the contraction property, so that the analysis must base its analysis on other procedures.
References
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Book

Dynamic Programming

TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI

Linear Programming and Sequential Decisions

TL;DR: In this paper, a typical sequential probabilistic model may be formulated in terms of a an initial decision rule and b a Markov process, and then optimized by means of linear programming.