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 ...
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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.
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Add this copy of Simulation-Based Optimization: Parametric Optimization to cart. $65.00, very good condition, Sold by Moe's Books rated 4.0 out of 5 stars, ships from Berkeley, CA, UNITED STATES, published 2015 by Springer.
Edition:
2nd Softcover Reprint of the Original 2nd 2015 edition
Publisher:
Springer
Published:
2015
Language:
English
Alibris ID:
17963548201
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Very good. No jacket. Second edition. Spine is cocked. Cover edges are slightly worn. Fore and bottom edges are stained, but readability is not impacted. Back binding glue is exposed. Inside is unmarked.
Add this copy of Simulation-Based Optimization: Parametric Optimization to cart. $66.37, good condition, Sold by HPB-Red rated 5.0 out of 5 stars, ships from Dallas, TX, UNITED STATES, published 2016 by Springer.
Edition:
2nd Softcover Reprint of the Original 2nd 2015 edition
Publisher:
Springer
Published:
2016
Language:
English
Alibris ID:
18080184393
Shipping Options:
Standard Shipping: $4.99
Trackable Expedited: $9.99
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Seller's Description:
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Add this copy of Simulation-Based Optimization: Parametric Optimization to cart. $116.39, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Newport Coast, CA, UNITED STATES, published 2016 by Springer.
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