Approximate Dynamic Programming: Solving the Curses of Dimensionality [Hardcover]

Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners.
Computing Reviews

This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems

Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming, Second Edition uniquely integrates four distinct disciplinesMarkov decision processes, mathematical programming, simulation, and statisticsto demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP.

The book continues to bridge the gap between computer science, simulation, and operations research and now adopts the notation and vocabulary of reinforcement learning as well as stochastic search and simulation optimization. The author outlines the essential algorithms that serve as a starting point in the design of practical solutions for real problems. The three curses of dimensionality that impact complex problems are introduced and detailed coverage of implementation challenges is provided. The Second Edition also features:

• A new chapter describing four fundamental classes of policies for working with diverse stochastic optimization problems: myopic policies, look-ahead policies, policy function approximations, and policies based on value function approximations

• A new chapter on policy search thatl³›