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Simulation-Based Optimization = Parametric Optimization Techniques and Reinforcement Learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Simulation-Based Optimization/ by Abhijit Gosavi.
其他題名:	Parametric Optimization Techniques and Reinforcement Learning /
作者:	Gosavi, Abhijit.
面頁冊數:	XXVI, 508 p. 42 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Operations research. -
電子資源:	https://doi.org/10.1007/978-1-4899-7491-4
ISBN:	9781489974914

Simulation-Based Optimization = Parametric Optimization Techniques and Reinforcement Learning /
Gosavi, Abhijit.

Simulation-Based OptimizationParametric Optimization Techniques and Reinforcement Learning /[electronic resource] :by Abhijit Gosavi. - 2nd ed. 2015. - XXVI, 508 p. 42 illus.online resource. - Operations Research/Computer Science Interfaces Series,551387-666X ;. - Operations Research/Computer Science Interfaces Series,59.

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.

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces 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.

ISBN: 9781489974914

Standard No.: 10.1007/978-1-4899-7491-4doiSubjects--Topical Terms:

573517
Operations research.

LC Class. No.: HD30.23

Dewey Class. No.: 658.40301

Simulation-Based Optimization = Parametric Optimization Techniques and Reinforcement Learning /
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