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This book represents an up-to-date, complete and accessible introduction to approximate dynamic programming. It is motivated primarily by problems that arise in operations research and engineering. Much of the emphasis in the book is placed on how to model complex problems and design practical, scalable algorithms for solving them. Example problems generally involve the management of physical, financial or informational resources in an industrial setting.
Mathematical statistics --- Dynamic programming --- Programmation dynamique --- Dynamic programming. --- 519.8 --- Operational research --- 519.8 Operational research --- Mathematical optimization --- Programming (Mathematics) --- Systems engineering
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The goal of this book is to enable readers to understand how to approach, model and solve a sequential decision problem. To that end, it uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem.
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Decision making --- Stochastic analysis. --- Reinforcement learning. --- Mathematical optimization. --- Statistical methods.
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