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Simulation-based optimization : parametric optimization techniques and reinforcement learning

Author: Abhijit Gosavi
Publisher: Boston : Springer, [2014] ©2015
Series: Operations research/computer science interfaces series, volume 55.
Edition/Format:   eBook : Document : English : Second editionView all editions and formats
Summary:
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques? especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Printed edition:
Print version:
Gosavi, Abhijit.
Simulation-based optimization.
New York : Springer, [2015]
(OCoLC)900549441
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Abhijit Gosavi
ISBN: 9781489974914 1489974911 1489974903 9781489974907
OCLC Number: 894691258
Description: 1 online resource (xxvii, 554 pages) : illustrations.
Contents: Background --
Simulation basics --
Simulation optimization: an overview --
Response surfaces and neural nets --
Parametric optimization --
Dynamic programming --
Reinforcement learning --
Stochastic search for controls --
Convergence: background material --
Convergence: parametric optimization --
Convergence: control optimization --
Case studies.
Series Title: Operations research/computer science interfaces series, volume 55.
Responsibility: Abhijit Gosavi.

Abstract:

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques? especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. 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 search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters? Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis? this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics.

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