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Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions - Couverture rigide
- ÉditeurJohn Wiley & Sons Inc
- Date d'édition2022
- ISBN 10 1119815037
- ISBN 13 9781119815037
- ReliureRelié
- Langueanglais
- Nombre de pages1136
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REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION: A UNIFIED FRAMEWORK FOR SEQUENTIAL DECISIONS
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Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. N° de réf. du vendeur ABNR-11585
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REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION: A UNIFIED FRAMEWORK FOR SEQUENTIAL DECISIONS
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Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions
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Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions
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Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions
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Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions
Edité par
Wiley, 2022
ISBN 10 : 1119815037
ISBN 13 : 9781119815037
Ancien ou d'occasion
Couverture rigide
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Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions
Edité par
Wiley, 2022
ISBN 10 : 1119815037
ISBN 13 : 9781119815037
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 42497144
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Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions
Edité par
John Wiley and Sons Ltd, 2022
ISBN 10 : 1119815037
ISBN 13 : 9781119815037
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Hardback. Etat : New. New copy - Usually dispatched within 4 working days. 454. N° de réf. du vendeur B9781119815037
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Reinforcement Learning and Stochastic Optimization (Hardcover)
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John Wiley & Sons Inc, New York, 2022
ISBN 10 : 1119815037
ISBN 13 : 9781119815037
Neuf
Couverture rigide
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Hardcover. Etat : new. Hardcover. REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of decision, information, decision, information, are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a "diary problem" that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781119815037
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Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions
Edité par
John Wiley & Sons, 2022
ISBN 10 : 1119815037
ISBN 13 : 9781119815037
Neuf
Couverture rigide
Vendeur : moluna, Greven, Allemagne
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Etat : New. Warren B. Powell, PhD, is Professor Emeritus of Operations Research and Financial Engineering at Princeton University, where he taught for 39 years. He was the founder and Director of CASTLE Laboratory, a research unit that works with industrial partners to. N° de réf. du vendeur 519700995
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