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Mean-Variance Optimization in Markov Decision Processes

2020-02-27 | |  58 |   41 |   0

Abstract

We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomized or history-based policies can improve performance. We prove that the complexity of computing a policy that maximizes the mean reward under a variance constraint is NP-hard for some cases, and strongly NPhard for others. We finally offer pseudopolynomial exact and approximation algorithms.

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