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Approximate dynamic programming : solving the curses of dimensionality

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