Mean-Variance Optimization in Markov Decision Processes

Details Mean-Variance Optimization in Markov Decision Processes Mean-Variance Optimization in Markov Decision Processes

2011-04-29

arxiv.org/abs/1104.5601v1

Computer Science

Learning

A full version of an ICML 2011 paper

Scientific paper

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 NP-hard for others. We finally offer pseudopolynomial exact and approximation algorithms.

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