LEADER 03968nam 2200541Ia 450 001 9910741168503321 005 20200520144314.0 010 $a1-4471-5022-8 024 7 $a10.1007/978-1-4471-5022-0 035 $a(OCoLC)829740706 035 $a(MiFhGG)GVRL6YOR 035 $a(CKB)2670000000530220 035 $a(MiAaPQ)EBC1205273 035 $a(EXLCZ)992670000000530220 100 $a20130228d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aSimulation-based algorithms for Markov decision processes /$fby Hyeong Soo Chang, Jiaqiao Hu, Michael C. Fu, Steven I. Marcus 205 $a2nd ed. 2013. 210 $aLondon $cSpringer$d2013 215 $a1 online resource (xvii, 229 pages) $cillustrations 225 1 $aCommunications and Control Engineering,$x0178-5354 300 $a"ISSN: 0178-5354." 311 $a1-4471-5021-X 311 $a1-4471-5990-X 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 410 0$aCommunications and control engineering. 606 $aMarkov processes 606 $aDecision making$xMathematical models 615 0$aMarkov processes. 615 0$aDecision making$xMathematical models. 676 $a658.4033 700 $aChang$b Hyeong Soo$01424776 701 $aHu$b Jiaqiao$01749939 701 $aFu$b Michael C$01749940 701 $aMarcus$b Steven I$0122139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741168503321 996 $aSimulation-based algorithms for Markov decision processes$94184400 997 $aUNINA