Algorithmic aspects of mean–variance optimization in Markov decision processes

Shie Mannor and John N. Tsitsiklis

European Journal of Operational Research, 2013, vol. 231, issue 3, 645-653

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

Keywords: Markov processes; Dynamic programming; Control; Complexity theory (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:231:y:2013:i:3:p:645-653

DOI: 10.1016/j.ejor.2013.06.019

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