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Simulation-based optimization = para...

Gosavi, Abhijit.

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Simulation-based optimization = parametric optimization techniques and reinforcement learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Simulation-based optimization/ by Abhijit Gosavi.
Reminder of title:	parametric optimization techniques and reinforcement learning /
Author:	Gosavi, Abhijit.
Published:	Boston, MA :Springer US : : 2015.,
Description:	xxvi, 508 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Probabilities. -
Online resource:	http://dx.doi.org/10.1007/978-1-4899-7491-4
ISBN:	9781489974914 (electronic bk.)

Simulation-based optimization = parametric optimization techniques and reinforcement learning /
Gosavi, Abhijit.

Simulation-based optimizationparametric optimization techniques and reinforcement learning /[electronic resource] :by Abhijit Gosavi. - 2nd ed. - Boston, MA :Springer US :2015. - xxvi, 508 p. :ill., digital ;24 cm. - Operations research/computer science interfaces series,v.551387-666X ;. - Operations research/computer science interfaces series ;v.55..

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.

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.

ISBN: 9781489974914 (electronic bk.)

Standard No.: 10.1007/978-1-4899-7491-4doiSubjects--Topical Terms:

518889
Probabilities.

LC Class. No.: TA340

Dewey Class. No.: 519.2

Simulation-based optimization = parametric optimization techniques and reinforcement learning /
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520 $a 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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