Algorithmic aspects of mean–variance optimization in Markov decision processes
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 th...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2017
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| Online Access: | http://hdl.handle.net/1721.1/108091 https://orcid.org/0000-0003-2658-8239 |