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Mean-Variance Optimization in Markov Decision Processes

Author(s)
Mannor, Shie; Tsitsiklis, John N.
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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 pseudo-polynomial exact and approximation algorithms.
Date issued
2011-06
URI
http://hdl.handle.net/1721.1/79401
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the Twenty-Eighth International Conference on Machine Learning, ICML 2011
Publisher
International Machine Learning Society
Citation
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.
Version: Author's final manuscript
ISBN
9781450306195
1450306195

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