Reinforcement Learning And Stochastic Optimization: A Unified Framework For Sequential Decisions

This book provides an overview of Reinforcement Learning and Stochastic Optimization, which is a single canonical framework that can model any sequential decision problem. It offers an explanation of the five core components (state variables, decision variables, exogenous information variables, transition function, and objective function) and provides an overview of the 15 distinct fields of research and eight distinct notational systems that are used to model these problems.