Simulation-Based Optimization
Parametric Optimization Techniques and Reinforcement Learning
(Sprache: Englisch)
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques - especially...
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce 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.
Inhaltsverzeichnis zu „Simulation-Based Optimization “
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.Autoren-Porträt von Abhijit Gosavi
Dr. A. Gosavi worked as an Assistant Professor in Colorado State University-Pueblo from Fall 2000 to Spring 2003 and as a visiting Assistant Professor in the same university from Fall 1999 to Spring 2000. Effective Fall 2008, he joined the Missouri University of Science And Technology as an assistant professor in the Department of Engineering Management and Systems Engineering. He obtained a Ph.D. in industrial engineering from the University of South Florida, and a BE and an M.Tech in Mechanical Engineering.He is the author of the book, Simulation-based optimization: Parametric optimization techniques and reinforcement learning, published by Springer in 2003 and is a member of INFORMS, IIE, ASEM, IEEE, POMS and ASEE.
Bibliographische Angaben
- Autor: Abhijit Gosavi
- 2014, 2. Aufl., XXVI, 508 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1489974903
- ISBN-13: 9781489974907
Sprache:
Englisch
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