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  • In this note, we consider discrete-time Markov decision processes with finite state space. Recalling explicit formulas for the growth rate of expected value and variance of the cumulative (random) reward, algorithmic procedures for finding optimal policies with respect to various mean variance optimality criteria are discussed. Computational experience with large scale numerical examples is reported.
  • In this note, we consider discrete-time Markov decision processes with finite state space. Recalling explicit formulas for the growth rate of expected value and variance of the cumulative (random) reward, algorithmic procedures for finding optimal policies with respect to various mean variance optimality criteria are discussed. Computational experience with large scale numerical examples is reported. (en)
Title
  • Mean variance optimality in Markov decision chains
  • Mean variance optimality in Markov decision chains (en)
skos:prefLabel
  • Mean variance optimality in Markov decision chains
  • Mean variance optimality in Markov decision chains (en)
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  • RIV/67985556:_____/05:00411443!RIV10-AV0-67985556
http://linked.open...avai/riv/aktivita
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  • P(GA402/05/0115), Z(AV0Z10750506)
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  • 529153
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  • RIV/67985556:_____/05:00411443
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  • Markov reward processes; expectation and variance of cumulative rewards (en)
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http://linked.open...ontrolniKodProRIV
  • [2D6DA3D7FDB8]
http://linked.open...v/mistoKonaniAkce
  • Hradec Králové
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  • Hradec Králové
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  • Proceedings of the 23rd International Conference Mathematical Methods in Economics 2005
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  • Sladký, Karel
  • Sitař, Milan
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http://linked.open...n/vavai/riv/zamer
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  • Gadeamus
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  • 978-80-7041-535-1
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