
Автор: Warren B. Powell
Издательство: Wiley
Год: 2022
Страниц: 1133
Язык: английский
Формат: pdf (true)
Размер: 31.4 MB
Clearing the jungle of stochastic optimization. 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. Each chapter 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.