New PDF release: Approximate dynamic programming. Solving the curses of

By Warren B. Powell

ISBN-10: 0470373067

ISBN-13: 9780470373064

Praise for the First Edition

"Finally, a ebook dedicated to dynamic programming and written utilizing the language of operations study (OR)! this gorgeous booklet fills a niche within the libraries of OR experts and practitioners."
Computing Reviews

This re-creation showcases a spotlight on modeling and computation for advanced sessions of approximate dynamic programming problems

Understanding approximate dynamic programming (ADP) is essential with a purpose to advance useful and fine quality ideas to complicated commercial difficulties, relatively whilst these difficulties contain making judgements within the presence of uncertainty. Approximate Dynamic Programming, moment version uniquely integrates 4 exact disciplines—Markov selection techniques, mathematical programming, simulation, and statistics—to exhibit the best way to effectively method, version, and clear up a variety of real-life difficulties utilizing ADP.

The booklet keeps to bridge the distance among desktop technological know-how, simulation, and operations study and now adopts the notation and vocabulary of reinforcement studying in addition to stochastic seek and simulation optimization. the writer outlines the fundamental algorithms that function a place to begin within the layout of sensible ideas for genuine difficulties. the 3 curses of dimensionality that influence advanced difficulties are brought and certain assurance of implementation demanding situations is equipped. The Second Edition additionally features:

  • A new bankruptcy describing 4 basic periods of guidelines for operating with varied stochastic optimization difficulties: myopic regulations, look-ahead guidelines, coverage functionality approximations, and rules in accordance with price functionality approximations

  • A new bankruptcy on coverage seek that brings jointly stochastic seek and simulation optimization strategies and introduces a brand new classification of optimum studying strategies

  • Updated insurance of the exploration exploitation challenge in ADP, now together with a lately built approach for doing lively studying within the presence of a actual nation, utilizing the idea that of the data gradient

  • A new series of chapters describing statistical tools for approximating worth services, estimating the worth of a set coverage, and price functionality approximation whereas looking for optimum policies

The awarded insurance of ADP emphasizes versions and algorithms, concentrating on similar functions and computation whereas additionally discussing the theoretical facet of the subject that explores proofs of convergence and cost of convergence. A comparable web site positive aspects an ongoing dialogue of the evolving fields of approximation dynamic programming and reinforcement studying, in addition to extra readings, software program, and datasets.

Requiring just a uncomplicated knowing of data and likelihood, Approximate Dynamic Programming, moment variation is a wonderful e-book for business engineering and operations examine classes on the upper-undergraduate and graduate degrees. It additionally serves as a worthwhile reference for researchers and execs who make the most of dynamic programming, stochastic programming, and regulate thought to resolve difficulties of their daily work.

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Extra info for Approximate dynamic programming. Solving the curses of dimensionality

Example text

The book integrates approximate dynamic programming with math programming, making it possible to solve intractably large deterministic or stochastic optimization problems. • We cover in depth the concept of the post-decision state variable, which plays a central role in our ability to solve problems with vector-valued decisions. The post-decision state offers the potential for dramatically simplifying many ADP algorithms by avoiding the need to compute a one-step transition matrix or otherwise approximate the expectation within Bellman’s equation.

1 do not work if the state variable is multidimensional. For example, instead of visiting node i in a network, we might visit state St = (St1 , St2 , . . , StB ), where Stb is the amount of blood on hand of type b. A variety of authors have independently discovered that an alternative strategy is to step forward through time, using iterative algorithms to help estimate 16 the challenges of dynamic programming the value function. This general strategy has been referred to as forward dynamic programming, incremental dynamic programming, iterative dynamic programming, adaptive dynamic programming, heuristic dynamic programming, reinforcement learning, and neuro-dynamic programming.

The reinforcement learning community focuses almost exclusively on problems with finite (and fairly small) sets of discrete actions. The control theory community is primarily interested in multidimensional and continuous actions (but not very many dimensions). In operations research it is not unusual to encounter problems where decisions are vectors with thousands of dimensions. As early as the 1950s the math programming community was trying to introduce uncertainty into mathematical programs. The resulting subcommunity is called stochastic programming and uses a vocabulary that is quite distinct from that of dynamic programming.

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Approximate dynamic programming. Solving the curses of dimensionality by Warren B. Powell

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