000 04337nam a22006135i 4500
001 978-1-84628-690-2
003 DE-He213
005 20161121231156.0
007 cr nn 008mamaa
008 100301s2007 xxk| s |||| 0|eng d
020 _a9781846286902
_9978-1-84628-690-2
024 7 _a10.1007/978-1-84628-690-2
_2doi
050 4 _aHD30.23
072 7 _aKJT
_2bicssc
072 7 _aKJMD
_2bicssc
072 7 _aBUS049000
_2bisacsh
082 0 4 _a658.40301
_223
100 1 _aChang, Hyeong Soo.
_eauthor.
245 1 0 _aSimulation-based Algorithms for Markov Decision Processes
_h[electronic resource] /
_cby Hyeong Soo Chang, Jiaqiao Hu, Michael C. Fu, Steven I. Marcus.
264 1 _aLondon :
_bSpringer London,
_c2007.
300 _aXVIII, 189 p. 38 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCommunications and Control Engineering,
_x0178-5354
505 0 _aMarkov Decision Processes -- Multi-stage Adaptive Sampling Algorithms -- Population-based Evolutionary Approaches -- Model Reference Adaptive Search -- On-line Control 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. It is well-known that many real-world problems modeled by MDPs have huge state and/or action spaces, leading to the notorious curse of dimensionality that makes practical solution of the resulting models intractable. In other cases, the system of interest is complex enough that it is not feasible to specify some of the MDP model parameters explicitly, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based numerical algorithms have been developed recently to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include: • multi-stage adaptive sampling; • evolutionary policy iteration; • evolutionary random policy search; and • model reference adaptive search. Simulation-based Algorithms for Markov Decision Processes brings this state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro-dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality. 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 instruction and reference for students of control and operations research.
650 0 _aBusiness.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aAlgorithms.
650 0 _aSystem theory.
650 0 _aManagement science.
650 0 _aProbabilities.
650 0 _aControl engineering.
650 1 4 _aBusiness and Management.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aControl.
650 2 4 _aSystems Theory, Control.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
700 1 _aHu, Jiaqiao.
_eauthor.
700 1 _aFu, Michael C.
_eauthor.
700 1 _aMarcus, Steven I.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781846286896
830 0 _aCommunications and Control Engineering,
_x0178-5354
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84628-690-2
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c509670
_d509670