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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning - Couverture souple
- ÉditeurSpringer
- Date d'édition2016
- ISBN 10 1489977317
- ISBN 13 9781489977311
- ReliureBroché
- Langueanglais
- Nombre de pages536
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series, 55)
Edité par
Springer, 2016
ISBN 10 : 1489977317
ISBN 13 : 9781489977311
Ancien ou d'occasion
paperback
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paperback. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! N° de réf. du vendeur S_348661241
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Edité par
Springer, 2015
ISBN 10 : 1489977317
ISBN 13 : 9781489977311
Ancien ou d'occasion
Soft cover
Vendeur : Moe's Books, Berkeley, CA, Etats-Unis
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Soft cover. Etat : 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. N° de réf. du vendeur 1147483
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series, 55)
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series, 55)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Etat : New. In. N° de réf. du vendeur ria9781489977311_new
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Simulation-Based Optimization
impression à la demandeVendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 536 pp. Englisch. N° de réf. du vendeur 9781489977311
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Simulation-Based Optimization
impression à la demandeVendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Brings the field completely up to dateAll computer code brought up to dateNew material not covered in first edition includes nested partitions, simultaneous perturbation, backtracking adaptive search and the stochastic ruler method. N° de réf. du vendeur 385644097
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Simulation-Based Optimization : Parametric Optimization Techniques and Reinforcement Learning
Edité par
Springer US, Springer US, 2016
ISBN 10 : 1489977317
ISBN 13 : 9781489977311
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. 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. N° de réf. du vendeur 9781489977311
Quantité disponible : 1 disponible(s)
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Edité par
Springer-Verlag New York Inc., 2016
ISBN 10 : 1489977317
ISBN 13 : 9781489977311
Neuf
Paperback / softback
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 813. N° de réf. du vendeur C9781489977311
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. 508. N° de réf. du vendeur 26375054675
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Simulation-based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 2nd reprint edition. 534 pages. 9.25x6.10x1.18 inches. In Stock. N° de réf. du vendeur x-1489977317
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