Автор: Hyeong Soo Chang; Michael C. Fu; Jiaqiao Hu; Steve Название: Simulation-based Algorithms for Markov Decision Processes ISBN: 1849966435 ISBN-13(EAN): 9781849966436 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.
Автор: Hyeong Soo Chang; Jiaqiao Hu; Michael C. Fu; Steve Название: Simulation-Based Algorithms for Markov Decision Processes ISBN: 144715990X ISBN-13(EAN): 9781447159902 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The updated 2nd edition of this book covers MDPs in constrained settings and with uncertain transition properties; approximation stochastic annealing, a population-based on-line simulation-based algorithm; game-theoretic method for solving MDPs and more.
Автор: Sheskin, Theodore J. (cleveland State University, Ohio, Usa) Название: Markov chains and decision processes for engineers and managers ISBN: 1420051113 ISBN-13(EAN): 9781420051117 Издательство: Taylor&Francis Рейтинг: Цена: 22202.00 р. Наличие на складе: Поставка под заказ.
Описание: Presents an introduction to finite Markov chains and Markov decision processes, with applications in engineering and management. This book introduces discrete-time, finite-state Markov chains, and Markov decision processes. It describes both algorithms and applications, enabling students to understand the logical basis for the algorithms.
Автор: Richard Boucherie; Nico M van Dijk Название: Markov Decision Processes in Practice ISBN: 3319477641 ISBN-13(EAN): 9783319477640 Издательство: Springer Рейтинг: Цена: 30745.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents classical Markov Decision Processes (MDP) for real-life applications and optimization. MDP allows users to develop and formally support approximate and simple decision rules, and this book showcases state-of-the-art applications in which MDP was key to the solution approach.
Описание: This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields.
Автор: J.A. V?sek Название: Transactions of the Tenth Prague Conferences ISBN: 9401082162 ISBN-13(EAN): 9789401082167 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: (More about Professor Spacek can be found in the Transactions of the Sixth Prague Conferen- ce) * The Tenth Prague Conference kept the traditional style and orien- tation typical for the previous Prague Conferences.
Описание: The book deals with a powerful and convenient approach to a great variety of types of problems of the recursive monte-carlo or stochastic approximation type. The approach, relating algorithm behavior to qualitative properties of deterministic or stochastic differ- ential equations, has advantages in algorithm conceptualiza- tion and design.
Описание: This book is an up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. The concentration of the book is on infinite-horizon discrete-time models, and it also discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models.
Автор: Qiying Hu; Wuyi Yue Название: Markov Decision Processes with Their Applications ISBN: 1441942386 ISBN-13(EAN): 9781441942388 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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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