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MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes

Applied Stochastic Models and Data Analysis

By:
DOI: 10.1002/asm.3150040405

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Abstract

Algorithms are described for determining optimal policies for finite state, finite action, infinite discrete time horizon Markov decision processes. Both value-improvement and policy-improvement techniques are used in the algorithms. Computing procedures are also described. The algorithms are appropriate for processes that are either finite or infinite, deterministic or stochastic, discounted or undiscounted, in any meaningful combination of these features. Computing procedures are described in terms of initial data processing, bound improvements, process reduction, and testing and solution. Application of the methodology is illustrated with an example involving natural resource management. Management implications of certain hypothesized relationships between mallard survival and harvest rates are addressed by applying the optimality procedures to mallard population models.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
MARKOV: A methodology for the solution of infinite time horizon MARKOV decision processes
Series title:
Applied Stochastic Models and Data Analysis
DOI:
10.1002/asm.3150040405
Volume
4
Issue:
4
Year Published:
1988
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
253-271
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Applied Stochastic Models and Data Analysis
First page:
253
Last page:
271