Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning
Language: English
Published by Springer, Humana, 2014
Series: Book 26 of 35 - Operations Research/Computer Science Interfaces
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Druck auf Anfrage Neuware - Printed after ordering - 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 EquationsThemed 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.…
Seller Inventory # 9781489974907
- Title
- Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning
- Author
- Abhijit Gosavi
- Publisher
- Springer, Humana
- Publication year
- 2014
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 1489974903
- ISBN 13
- 9781489974907
- Edition
- 2nd Edition
- Item weight
- 963 grams
- Dimensions
- 241x160x35 mm
- Series
- Book 26 of 35: Operations Research/Computer Science Interfaces
Stochastic Control Optimization:
- An Accessible Introduction to Reinforcement Learning Techniques for Solving Markov Decision Processes (MDPs), with Step-by-Step Descriptions of Numerous Algorithms, e.g., Q-Learning, SARSA, R-SMART, Actor-Critics, Q-P-Learning, and Classical Approximate Policy Iteration
- A Detailed Discussion on Dynamic Programing for Solving MDPs and Semi-MDPs (SMDPs), Including Steps for Value Iteration and Policy Iteration
- An Introduction to Function Approximation with Reinforcement Learning
- An In-Depth Treatment of Reinforcement Learning Methods for SMDPs, Average Reward Problems, Finite Horizon Problems, and Two Time Scales
- Computer Programs (available online)
- A Gentle Introduction to Convergence Analysis via Banach Fixed Point Theory and Ordinary Differential Equations (ODEs)
- A Step-by-Step Description of Stochastic Adaptive Search Algorithms, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Stochastic Ruler, and Meta-Heuristics, e.g., Simulated Annealing, Tabu Search, and Genetic Algorithms
- A Clear and Simple Introduction to the Methodology of Neural Networks
The book was written for students and researchers in the fields ofengineering (industrial, electrical, and computer), computer science,operations research, management science, and applied mathematics. Anattractive feature of this book is its accessibility to readers new to this topic.
"Synopsis" may belong to another edition of this title.
About the Author
He has published more than fifty journal and conference articles - many of which have appeared in leading scholarly journals such as Management Science, Automatica, INFORMS Journal on Computing, Machine Learning, Journal of Retailing, Systems and Control Letters and the European Journal of Operational Research.
"The statements of science are not of what is true and what is not true, but statements of what is known with different degrees of certainty." --- Richard Feynman
"About the title" may belong to another edition of this title.
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