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Trading Performance for Stability in Markov Decision Processes

Abstract:

We study the complexity of central controller synthesis problems for finite-state Markov decision processes, where the objective is to optimize both the expected mean-payoff performance of the system and its stability. We argue that the basic theoretical notion of expressing the stability in terms of the variance of the mean-payoff (called global variance in our paper) is not always sufficient, since it ignores possible instabilities on respective runs. For this reason we propose alernative...

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Publisher copy:
10.1109/LICS.2013.39

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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
Volume:
abs/1305.4103
Pages:
331-340
Publication date:
2013-04-23
DOI:
ISSN:
1043-6871
URN:
uuid:6c26d178-5cf4-414a-8171-9a3757b3d472
Source identifiers:
407462
Local pid:
pubs:407462
Keywords:

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