Handbook Of Learning And Approximate Dynamic Programming

Author: Jennie Si
Publisher: John Wiley & Sons
ISBN: 9780471660545
Size: 75.60 MB
Format: PDF
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In this volume, leading experts in the field summarize the latest research in areas including: Reinforcement learning and its relationship to supervised learning Model-based adaptive critic designs Direct neural dynamic programming ...

Approximate Dynamic Programming

Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 111802916X
Size: 63.72 MB
Format: PDF, ePub, Docs
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This beautiful book fills a gap in the libraries of OR specialists and practitioners." —Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems ...

Simulation Based Optimization

Author: Abhijit Gosavi
Publisher: Springer
ISBN: 1489974911
Size: 47.79 MB
Format: PDF, ePub
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Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive ...

Approximate Dynamic Programming

Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 9780470182956
Size: 67.12 MB
Format: PDF, ePub, Mobi
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This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems ...

Convex Methods For Approximate Dynamic Programming

Author: Arezou Keshavarz
Size: 30.54 MB
Format: PDF, Kindle
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In such cases, approximate dynamic programming (ADP) gives a method for finding a good, if not optimal, policy. In this work, we rely on our ability to (numerically) solve convex optimization problems with great speed and reliability.

Stochastic Decomposition

Author: Julia L. Higle
Publisher: Springer Science & Business Media
ISBN: 9780792338406
Size: 62.69 MB
Format: PDF, Docs
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This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms.

Anticipatory Optimization For Dynamic Decision Making

Author: Stephan Meisel
Publisher: Springer Science & Business Media
ISBN: 146140505X
Size: 29.59 MB
Format: PDF, ePub
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Meisel S, Suppa U, Mattfeld D (2010) GRASP based approximate dynamic
programming for dynamic routing of a vehicle. In: Casatera M, Voss S (eds) Post
Conference Volume of the VIII Metaheuristic International Conference, Springer,
Berlin ...