Название: Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential DecisionsАвтор: Warren B. Powell
Издательство: Wiley
Год: 2022
Страниц: 1133
Язык: английский
Формат: pdf (true)
Размер: 31.4 MB
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. 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. Each chapter (except for chapters 1 and 7) illustrates how to model and solve a specific decision problem. These have been designed to bring out the features of different classes of policies. There are Python modules that go with most of these exercises that provide an opportunity to do computational work. These exercises will generally require that the reader use the Python module as a start, but where additional programming is required.
The Audience:
This book is aimed at readers who want to develop models that are practical, flexible, scalable, and implementable for sequential decision problems in the presence of different forms of uncertainty. The ultimate goal is to create software tools that can solve real problems. We use careful mathematical modeling as a necessary step for translating real problems into software. The readers who appreciate both of these goals will enjoy our presentation the most.
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