A modified dynamic programming method for markovian decision problems
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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.About:
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.read more
Citations
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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
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
Awi Federgruen,P. J. Schweitzer +1 more
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
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.