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008 150903s2008 xxu| o |||| 0|eng d
020 _a9780387369518
_99780387369518
024 7 _a10.1007/9780387369518
_2doi
035 _avtls000331336
039 9 _a201509030225
_bVLOAD
_c201404121824
_dVLOAD
_c201404091552
_dVLOAD
_c201401311411
_dstaff
_y201401301207
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA402-402.37
100 1 _aHu, Qiying.
_eautor
_9301651
245 1 0 _aMarkov Decision Processes With Their Applications /
_cby Qiying Hu, Wuyi Yue.
264 1 _aBoston, MA :
_bSpringer US,
_c2008.
300 _axvI, 298 páginas,
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aAdvances in Mechanics and Mathematics ;
_v14
500 _aSpringer eBooks
505 0 _aDiscretetimemarkovdecisionprocesses: Total Reward -- Discretetimemarkovdecisionprocesses: Average Criterion -- Continuous Time Markov Decision Processes -- Semi-Markov Decision Processes -- Markovdecisionprocessesinsemi-Markov Environments -- Optimal control of discrete event systems: I -- Optimal control of discrete event systems: II -- Optimal replacement under stochastic Environments -- Optimalal location in sequential online Auctions.
520 _aMarkov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters. Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems: *a new methodology for MDPs with discounted total reward criterion; *transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; *MDPs in stochastic environments, which greatly extends the area where MDPs can be applied; *applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions. This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aYue, Wuyi.
_eautor
_9299806
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387369501
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-36951-8
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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