Markov decision processes with imprecise transition probabilities

Markov decision processes with imprecise transition probabilities

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Article ID: iaor1995336
Country: United States
Volume: 42
Issue: 4
Start Page Number: 739
End Page Number: 749
Publication Date: Jul 1994
Journal: Operations Research
Authors: ,
Keywords: programming: dynamic
Abstract:

The authors present new numerical algorithms and bounds for the infinite horizon, discrete stage, finite stage and action Markov decision process with imprecise transition probabilities. They assume that the transition probability mass vector for each state and action is described by a finite number of linear inequalities. This model of imprecision appears to be well suited for describing statistically determined confidence limits and/or natural language statements of likelihood. The numerical procedures for calculating an optimal max-min strategy are based on successive approximations, reward revision, and modified policy iteration. The bounds that are determined are at least as tight as currently available bounds for the case where the transition probabilities are precise.

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