Markov Decision Processes With Their Applications
Markov 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,...
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
New York, NY :
Springer US
2008.
Cham : Springer Nature |
| Serier: | Advances in Mechanics and Mathematics
14 |
| Fag: | |
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Markov Decision Processes With Their Applications, Texte imprimé, 9780387369501 • Markov Decision Processes with Their Applications, Texte imprimé, 9780387515465 • Markov Decision Processes with Their Applications, Texte imprimé, 9781441942388 |
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| 100 | 1 | |a Hu, Qiying. | |
| 245 | 1 | 0 | |a Markov Decision Processes With Their Applications |c by Qiying Hu,... Wuyi Yue,... |
| 260 | |a New York, NY : |b Springer US. | ||
| 260 | |a Cham : |b Springer Nature, |c 2008. | ||
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| 505 | 0 | |a Discretetimemarkovdecisionprocesses: 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Markov 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. | ||
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| 650 | |a Markov, processus de | ||
| 650 | |a Optimisation mathématique | ||
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| 776 | 0 | |t Markov Decision Processes with Their Applications |b Texte imprimé |z 9781441942388 | |
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