This groundbreaking book uniquely integrates four distinct disciplinesMarkov design processes, mathematical programming, simulation, and statisticsto demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. Now, in the second edition, two new chapters have been added on programming with uncertain parameters and optimization to reflect the growing practical influence of ADP in multiple settings. Extensive modifications have also been made throughout the entire volume. Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book’s main concepts.

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