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38

CORR
2011
Springer

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Mean-Variance Optimization in Markov Decision Processes

13 years 6 months ago

Mean-Variance Optimization in Markov Decision Processes

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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.

Shie Mannor, John N. Tsitsiklis

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CORR 2011 | Cumulative Reward | Education | Horizon Markov Decision | Strongly Np-hard |

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Added	13 May 2011
Updated	13 May 2011
Type	Journal
Year	2011
Where	CORR
Authors	Shie Mannor, John N. Tsitsiklis

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