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Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling, B?r


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Автор: B?r
Название:  Generic Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling
ISBN: 9783658391782
Издательство: Springer
Классификация:



ISBN-10: 3658391782
Обложка/Формат: Soft cover
Страницы: 148
Вес: 0.23 кг.
Дата издания: 16.10.2022
Язык: English
Издание: 1st ed. 2022
Иллюстрации: 35 illustrations, color; 4 illustrations, black and white; xxii, 148 p. 39 illus., 35 illus. in color.
Размер: 210 x 148
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The production control of flexible manufacturing systems is a relevant component that must go along with the requirements of being flexible in terms of new product variants, new machine skills and reaction to unforeseen events during runtime. This work focuses on developing a reactive job-shop scheduling system for flexible and re-configurable manufacturing systems. Reinforcement Learning approaches are therefore investigated for the concept of multiple agents that control products including transportation and resource allocation.
Дополнительное описание: Introduction.- Requirements for Production Scheduling in Flexible Manufacturing.- Reinforcement Learning as an Approach for Flexible Scheduling.- Concept for Multi-Resources Flexible Job-Shop Scheduling.- Multi-Agent Approach for Reactive Scheduling in Fl



Multi-Agent Machine Learning

Автор: Schwartz H M
Название: Multi-Agent Machine Learning
ISBN: 111836208X ISBN-13(EAN): 9781118362082
Издательство: Wiley
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Цена: 15198.00 р.
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Описание: The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces.

Deep Reinforcement Learning with Guaranteed Performance

Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou
Название: Deep Reinforcement Learning with Guaranteed Performance
ISBN: 3030333833 ISBN-13(EAN): 9783030333836
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution.Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

Deep Reinforcement Learning with Guaranteed Performance

Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou
Название: Deep Reinforcement Learning with Guaranteed Performance
ISBN: 3030333868 ISBN-13(EAN): 9783030333867
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book is devoted to the description of the most widely used classifications of the most frequent fractures in clinical practice. For each type of fracture one or several classifications are described.This edition will include new classifications and classifications that have gained popularity in the last 3 years, resulting in 25% new material.


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