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Description
  • Learning of autonomous agents during their interaction with the environment is one of the frequent tasks in machine learning. However, the efficiency of the learning process drops dramatically with the complexity of the problem domain. In this paper, we present a survey of existing methods for reduction of the dimensionality of the state space.
  • Learning of autonomous agents during their interaction with the environment is one of the frequent tasks in machine learning. However, the efficiency of the learning process drops dramatically with the complexity of the problem domain. In this paper, we present a survey of existing methods for reduction of the dimensionality of the state space. (en)
  • Učení autonomních agentů na základě jejich interakce s prostředím, ve kterém se vyskytují, patří mezi časté úkoly strojového učení. Efektivita tohoto procesu ale výrazně klesá s rostoucí složitostí problémové domény. Tento článek přináší přehled metod sloužících k redukci dimenzionality stavového prostoru. (cs)
Title
  • State Space Dimensionality Reduction in Markov Decision Processes
  • Redukce dimenzionality stavového prostoru v Markovovských rozhodovacích procesech (cs)
  • State Space Dimensionality Reduction in Markov Decision Processes (en)
skos:prefLabel
  • State Space Dimensionality Reduction in Markov Decision Processes
  • Redukce dimenzionality stavového prostoru v Markovovských rozhodovacích procesech (cs)
  • State Space Dimensionality Reduction in Markov Decision Processes (en)
skos:notation
  • RIV/00216208:11320/08:00100166!RIV09-MSM-11320___
http://linked.open...avai/riv/aktivita
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  • Z(MSM0021620838)
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  • 397252
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  • RIV/00216208:11320/08:00100166
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  • State; Space; Dimensionality; Reduction; Markov; Decision; Processes (en)
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  • [8AE95F0783EB]
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  • Praha
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  • Praha
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  • WDS'08 Proceedings of Contributed Papers
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  • Sýkora, Ondřej
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number of pages
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  • Matfyzpress
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  • 978-80-7378-065-4
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  • 11320
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