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 LEADER 02221nam 22004933 450 001 9911006688603321 005 20231110233701.0 010 $a9781523149759 010 $a1523149752 010 $a9789201199225 010 $a9201199228 035 $a(MiAaPQ)EBC7152685 035 $a(Au-PeEL)EBL7152685 035 $a(CKB)25610225200041 035 $a(NjHacI)9925610225200041 035 $a(MiAaPQ)EBC32156579 035 $a(Au-PeEL)EBL32156579 035 $a(EXLCZ)9925610225200041 100 $a20221210d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aResource Requirements for Nuclear Power Infrastructure Development 205 $a1st ed. 210 1$aVienna :$cInternational Atomic Energy Agency,$d2022. 210 4$dİ2022. 215 $a1 online resource (50 pages) 225 1 $aIAEA Nuclear Energy Series No ;$vv.NG-T-3.21 311 08$aPrint version: IAEA Resource Requirements for Nuclear Power Infrastructure Development Vienna : International Atomic Energy Agency,c2022 9789201198228 330 $a"Developing a nuclear power programme is a major undertaking requiring careful planning and preparation. This publication provides guidance for Member States that with to assess the resources required for the development of the infrastructure needed for a nuclear power programme. Resource estimates are presented in person years, to account for economic differences across countries, in terms of labour costs, which may vary significantly. The data are presented in sufficient detail that they can also be used by countries that have decided to expand their nuclear programme after a long period without building any new nuclear power plants."--Publisher's description. 410 0$aIAEA Nuclear Energy Series No 606 $aNuclear power plants 615 0$aNuclear power plants. 676 $a621.48 700 $aIAEA$01594159 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911006688603321 996 $aResource Requirements for Nuclear Power Infrastructure Development$94389383 997 $aUNINA