By Warren B. Powell(auth.), Walter A. Shewhart, Samuel S. Wilks(eds.)
Praise for the First Edition
"Finally, a publication dedicated to dynamic programming and written utilizing the language of operations examine (OR)! this gorgeous publication fills a spot within the libraries of OR experts and practitioners."
This new version showcases a spotlight on modeling and computation for complicated periods of approximate dynamic programming problems
figuring out approximate dynamic programming (ADP) is key that allows you to boost sensible and top of the range recommendations to advanced commercial difficulties, fairly while these difficulties contain making judgements within the presence of uncertainty. Approximate Dynamic Programming, moment variation uniquely integrates 4 exact disciplines—Markov determination procedures, mathematical programming, simulation, and statistics—to show how you can effectively method, version, and resolve quite a lot of real-life difficulties utilizing ADP.
The ebook keeps to bridge the distance among computing device technology, 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 functional options for genuine difficulties. the 3 curses of dimensionality that impression complicated difficulties are brought and particular insurance of implementation demanding situations is equipped. The Second Edition additionally beneficial properties:
a brand new bankruptcy describing 4 primary periods of rules for operating with varied stochastic optimization difficulties: myopic rules, look-ahead regulations, coverage functionality approximations, and guidelines in response to worth functionality approximations
a brand new bankruptcy on coverage seek that brings jointly stochastic seek and simulation optimization techniques and introduces a brand new classification of optimum studying ideas
up to date insurance of the exploration exploitation challenge in ADP, now together with a lately built procedure for doing energetic studying within the presence of a actual nation, utilizing the concept that of the information gradient
a brand new series of chapters describing statistical equipment for approximating worth capabilities, estimating the worth of a hard and fast coverage, and price functionality approximation whereas looking for optimum guidelines
The provided assurance of ADP emphasizes types and algorithms, concentrating on comparable purposes and computation whereas additionally discussing the theoretical facet of the subject that explores proofs of convergence and cost of convergence. A similar site beneficial properties 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 simple knowing of information and likelihood, Approximate Dynamic Programming, moment variation is a superb ebook 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 keep watch over idea to resolve 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 selection methods (pages 57–109):
Chapter four creation to Approximate Dynamic Programming (pages 111–165):
Chapter five Modeling Dynamic courses (pages 167–219):
Chapter 6 rules (pages 221–248):
Chapter 7 coverage seek (pages 249–288):
Chapter eight Approximating worth 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 price 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):
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Extra info for Approximate Dynamic Programming: Solving the Curses of Dimensionality, Second Edition
This chapter describes a series of approximation techniques that are directly applicable for these problems. Chapter 14 There are many problems that can be described under the umbrella of “resource allocation” that offer special structure that we can exploit. These problems tend to be high-dimensional, with state variables that can easily have thousands or even millions of dimensions. However, when we combine concavity with the post-decision state variable, we produce algorithms that can handle industrial-strength applications.
Computer scientists, especially those working in the realm of artificial intelligence, found that dynamic programming was a useful framework for approaching certain classes of machine learning problems known as reinforcement learning. As different communities discovered the same concepts and algorithms, they invented their own vocabularies to go with them. As a result we can solve the Bellman equations, the Hamiltonian, the Jacobian, the Hamilton–Jacobian, or the allpurpose Hamilton–Jacobian–Bellman equations (typically referred to as the HJB equations).
We avoid many of the rich modeling and algorithmic issues that arise in more realistic problems. Chapter 1 Here we set the tone for the book, introducing the challenge of the three “curses of dimensionality” that arise in complex systems. Chapter 2 Dynamic programs are best taught by example. Here we describe three classes of problems: deterministic problems, stochastic problems, and information acquisition problems. Notation is kept simple but precise, and readers see a range of different applications.