000			04029nam a22004215i 4500
001			286475
003			MX-SnUAN
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007			cr nn 008mamaa
008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447150220 _99781447150220
024	7		_a10.1007/9781447150220 _2doi
035			_avtls000339984
039		9	_a201509030320 _bVLOAD _c201404300408 _dVLOAD _y201402061014 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTJ212-225
100	1		_aChang, Hyeong Soo. _eautor _9315112
245	1	0	_aSimulation-Based Algorithms for Markov Decision Processes / _cby Hyeong Soo Chang, Jiaqiao Hu, Michael C. Fu, Steven I. Marcus.
250			_a2nd ed. 2013.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_axvii, 229 páginas 29 ilustraciones, 1 ilustraciones en color. _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		_aCommunications and Control Engineering, _x0178-5354
500			_aSpringer eBooks
505	0		_aMarkov Decision Processes -- Multi-stage Adaptive Sampling Algorithms -- Population-based Evolutionary Approaches -- Model Reference Adaptive Search -- On-line Control Methods via Simulation -- Game-theoretic Methods via Simulation.
520			_aMarkov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.  Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable.  In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function.  Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: . innovative material on MDPs, both in constrained settings and with uncertain transition properties; . game-theoretic method for solving MDPs; . theories for developing roll-out based algorithms; and . details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research. The Communications and Control Engineering series reports major technological advances which have potential for great impact in the fields of communication and control. It reflects research in industrial and academic institutions around the world so that the readership can exploit new possibilities as they become available.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aHu, Jiaqiao. _eautor _9315113
700	1		_aFu, Michael C. _eautor _9304348
700	1		_aMarcus, Steven I. _eautor _9315114
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447150213
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-5022-0 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c286475 _d286475