9781489974907 - simulationbased optimization: parametric optimization techniques and reinforcement learning: 55 di gosavi (20 risultati)
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Buch. Condizione: 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 f…or 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.
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Editore: SPRINGER, 2015
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Lingua: Inglese
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Serie: Libro 26 di 35 - Operations Research/Computer Science Interfaces
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Condizione: new. Questo è un articolo print on demand.
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Condizione: 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 stochast…ic ruler method.
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Editore: Springer, Humana Okt 2014, 2014
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Buch. Condizione: 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 - espec…ially 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.
Lingua: Inglese
Editore: Humana, 2014
Serie: Libro 26 di 35 - Operations Research/Computer Science Interfaces
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Buch. Condizione: Neu. Simulation-Based Optimization | Parametric Optimization Techniques and Reinforcement Learning | Abhijit Gosavi | Buch | Operations Research/Computer Science Interfaces Series | xxvi | Englisch | 2014 | Humana | EAN 9781489974907 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17,…69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.
Lingua: Inglese
Editore: Springer, Humana Okt 2014, 2014
Serie: Libro 26 di 35 - Operations Research/Computer Science Interfaces
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 ¿ especiall…y 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 536 pp. Englisch.
