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001 394528
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008 221202s2022 sz | s |||| 0|eng d
020 _a9783031076145
024 7 _a10.1007/978-3-031-07614-5
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
100 1 _aDimitrakakis, Christos
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDecision Making Under Uncertainty and Reinforcement Learning
_bTheory and Algorithms
_cby Christos Dimitrakakis, Ronald Ortner
250 _a1st edition 2022
264 1 _aCham
_bSpringer International Publishing
_c2022
300 _a1 recurso en línea (XIII, 243 páginas)
_b67 ilustraciones, 62 ilustraciones a color
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 0 _aIntelligent Systems Reference Library
_x1868-4408
_v223
505 0 _aIntroduction -- Subjective probability and utility -- Decision problems -- Estimation. .
520 _aThis book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in introductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning. .
700 1 _aOrtner, Ronald
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
776 0 8 _iPrinted edition:
_z9783031076121
776 0 8 _iPrinted edition:
_z9783031076138
776 0 8 _iPrinted edition:
_z9783031108921
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-07614-5
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
988 _aSpringer_Robotics_2022
999 _c394528
_d394528