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powell w reinforcement learning stochastic optimiz 2022

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Textbook in PDF format 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, 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 into four fundamental classes that span every method suggested in the academic literature or used in practice. Sequential Decision Problems Canonical Problems and Applications Online Learning Introduction to Stochastic Search Derivative-Based Stochastic Search Stepsize Policies Derivative-Free Stochastic Search State-dependent Problems Modeling Sequential Decision Problems Uncertainty Modeling Designing Policies Policy Function Approximations and Policy Search Cost Function Approximations Exact Dynamic Programming Backward Approximate Dynamic Programming Forward ADP I: The Value of a Policy Forward ADP II: Policy Optimization Forward ADP III: Convex Resource Allocation Problems Direct Lookahead Policies Multiagent Modeling and Learning

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powell w reinforcement learning stochastic optimiz 2022

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