By Warren B. Powell(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)
Praise for the First Edition
"Finally, a booklet dedicated to dynamic programming and written utilizing the language of operations examine (OR)! this gorgeous booklet fills a spot within the libraries of OR experts and practitioners."
This new version showcases a spotlight on modeling and computation for advanced periods of approximate dynamic programming problems
knowing approximate dynamic programming (ADP) is essential with a view to advance useful and fine quality strategies to advanced business difficulties, really whilst these difficulties contain making judgements within the presence of uncertainty. Approximate Dynamic Programming, moment variation uniquely integrates 4 precise disciplines—Markov choice strategies, mathematical programming, simulation, and statistics—to display how you can effectively process, version, and clear up quite a lot of real-life difficulties utilizing ADP.
The e-book maintains to bridge the distance among laptop 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 kick off point within the layout of functional suggestions for actual difficulties. the 3 curses of dimensionality that impression advanced difficulties are brought and designated assurance of implementation demanding situations is supplied. The Second Edition additionally gains:
a brand new bankruptcy describing 4 basic periods of guidelines for operating with assorted stochastic optimization difficulties: myopic regulations, look-ahead rules, coverage functionality approximations, and regulations in line with worth functionality approximations
a brand new bankruptcy on coverage seek that brings jointly stochastic seek and simulation optimization suggestions and introduces a brand new classification of optimum studying recommendations
up to date assurance of the exploration exploitation challenge in ADP, now together with a lately constructed technique for doing energetic studying within the presence of a actual nation, utilizing the idea that of the information gradient
a brand new series of chapters describing statistical tools for approximating price services, estimating the price of a set coverage, and price functionality approximation whereas looking for optimum regulations
The offered assurance of ADP emphasizes types and algorithms, targeting comparable purposes and computation whereas additionally discussing the theoretical part of the subject that explores proofs of convergence and expense of convergence. A similar site gains an ongoing dialogue of the evolving fields of approximation dynamic programming and reinforcement studying, besides extra readings, software program, and datasets.
Requiring just a easy knowing of statistics and likelihood, Approximate Dynamic Programming, moment variation is a wonderful e-book for business engineering and operations study classes on the upper-undergraduate and graduate degrees. It additionally serves as a precious reference for researchers and execs who make the most of dynamic programming, stochastic programming, and keep an eye on thought to unravel difficulties of their daily work.Content:
Chapter 1 The demanding situations of Dynamic Programming (pages 1–23):
Chapter 2 a few Illustrative types (pages 25–56):
Chapter three advent to Markov choice strategies (pages 57–109):
Chapter four advent to Approximate Dynamic Programming (pages 111–165):
Chapter five Modeling Dynamic courses (pages 167–219):
Chapter 6 regulations (pages 221–248):
Chapter 7 coverage seek (pages 249–288):
Chapter eight Approximating price capabilities (pages 289–336):
Chapter nine studying price functionality Approximations (pages 337–381):
Chapter 10 Optimizing whereas studying (pages 383–418):
Chapter eleven Adaptive Estimation and Stepsizes (pages 419–456):
Chapter 12 Exploration as opposed to Exploitation (pages 457–496):
Chapter thirteen worth functionality Approximations for source Allocation difficulties (pages 497–539):
Chapter 14 Dynamic source Allocation difficulties (pages 541–592):
Chapter 15 Implementation demanding situations (pages 593–606):
Read or Download Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition PDF
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Compliment for the 1st variation "Finally, a e-book dedicated to dynamic programming and written utilizing the language of operations examine (OR)! this pretty ebook fills a niche within the libraries of OR experts and practitioners. " —Computing ReviewsThis new version showcases a spotlight on modeling and computation for complicated sessions of approximate dynamic programming difficulties knowing approximate dynamic programming (ADP) is key so that it will enhance useful and top quality suggestions to complicated business difficulties, rather while these difficulties contain making judgements within the presence of uncertainty.
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Additional resources for Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition
1 The Shortest Path Problem Perhaps one of the most popular dynamic programming problems is known as the shortest path problem. Although it has a many applications, it is easiest to describe in terms of the problem faced by every driver when finding a path from one location to the next over a road network. Let I = the set of nodes (intersections) in the network, L = the set of links (i, j ) in the network, cij = the cost (typically the time) to drive from node i to node j, i, j ∈ I, (i, j ) ∈ L, I+ i = the set of nodes j for which there is a link (i, j ) ∈ L, I− j = the set of nodes i for which there is a link (i, j ) ∈ L.
6 The Asset Acquisition Problem II Many asset acquisition problems introduce additional sources of uncertainty. The assets we are acquiring could be stocks, planes, energy commodities such as oil, consumer goods, and blood. In addition to the need to satisfy random demands (the only source of uncertainty we considered in our basic asset acquisition problem), we might have randomness in the prices at which we buy and sell assets. We may also include exogenous changes to the assets on hand due to additions (cash deposits, blood donations, energy discoveries) and subtractions (cash withdrawals, equipment failures, theft of product).
For this reason we adopt two notational styles for modeling decisions: a for discrete actions common in the models solved in reinforcement learning, and x for the continuous and sometimes high-dimensional decision vectors common in operations research and math programming. Throughout the book, we use action a as our default notation, but switch to x in the context of applications that require continuous or multidimensional decisions. Some other important features of this book are as follows: • We identify the three curses of dimensionality that characterize some dynamic programs, and develop a comprehensive algorithmic strategy for overcoming them.