Approximate dynamic programming : solving the curses of dimensionality

By:

Powell, Warren B

Material type: Text

TextPublication details: New Jersey Wiley 2011Edition: 2nd edDescription: xviii. 627pISBN:

9780470604458 (hb.)

Subject(s):

DDC classification:

519.703 POW

Summary: "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 disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to 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"

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Book	Plaksha University Library	Mathematics	519.703 POW (Browse shelf(Opens below))	Available		002755
Book	Plaksha University Library	Mathematics	519.703 POW (Browse shelf(Opens below))	Available		002756

https://www.wiley.com/en-us/Approximate+Dynamic+Programming%3A+Solving+the+Curses+of+Dimensionality%2C+2nd+Edition-p-9780470604458

"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 disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to 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"

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