Simulation-based optimization : parametric optimization techniques and reinforcement learning / Abhijit Gosavi.
"Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail aremodel-free optimization techniques – especially designed for those discrete-event, stochastic syste...
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| Main Author: | |
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| Format: | Ebook |
| Language: | English |
| Published: |
Boston :
Springer,
[2014]
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| Edition: | Second edition. |
| Series: | Operations research/computer science interfaces series ;
volume 55. |
| Subjects: | |
| Online Access: | Springer eBooks |
| Summary: | "Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail aremodel-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-SMARTalgorithms, 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 Learningand 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."--Publisher's website. |
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| Physical Description: | 1 online resource (xxvii, 554 pages) : illustrations. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 1489974903 9781489974907 1489974911 9781489974914 |