Mean-Variance Optimization in Markov Decision Processes
Author(s)Mannor, Shie; Tsitsiklis, John N.
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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 pseudo-polynomial exact and approximation algorithms.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the Twenty-Eighth International Conference on Machine Learning, ICML 2011
International Machine Learning Society
Mannor, Shie and John Tsitsiklis. "Mean-Variance Optimization in Markov Decision Processes ." in Twenty-Eighth International Conference on Machine Learning, ICML 2011, Jun. 28-Jul.2, Bellevue, Washington. 2011.
Author's final manuscript