Описание: This cookbook will help you to gain a solid understanding of deep reinforcement learning (RL) algorithms with the help of concise, easy-to-follow implementations from scratch. You`ll learn how to implement these algorithms with minimal code and develop AI applications to solve real-world and business problems using RL.
Автор: Sadhu Arup Kumar, Konar Amit Название: Multi-Agent Coordination: A Reinforcement Learning Approach ISBN: 1119699037 ISBN-13(EAN): 9781119699033 Издательство: Wiley Рейтинг: Цена: 17258.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Discover the latest developments in multi-robot coordination techniques with this insightful and original resource
Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms.
You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field.
Readers will discover cutting-edge techniques for multi-agent coordination, including:
An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium
Improving convergence speed of multi-agent Q-learning for cooperative task planning
Consensus Q-learning for multi-agent cooperative planning
The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning
A modified imperialist competitive algorithm for multi-agent stick-carrying applications
Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
Автор: Mohit Sewak Название: Deep Reinforcement Learning ISBN: 9811382840 ISBN-13(EAN): 9789811382840 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Нет в наличии.
Описание: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code.This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.
Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou Название: Deep Reinforcement Learning with Guaranteed Performance ISBN: 3030333833 ISBN-13(EAN): 9783030333836 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Lonza, Andrea Название: Reinforcement learning algorithms with python ISBN: 1789131111 ISBN-13(EAN): 9781789131116 Издательство: Неизвестно Рейтинг: Цена: 7539.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.
Автор: Belousov Boris, Abdulsamad Hany, Klink Pascal Название: Reinforcement Learning Algorithms: Analysis and Applications ISBN: 3030411877 ISBN-13(EAN): 9783030411879 Издательство: Springer Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences.
Автор: Belousov Boris, Abdulsamad Hany, Klink Pascal Название: Reinforcement Learning Algorithms: Analysis and Applications ISBN: 3030411907 ISBN-13(EAN): 9783030411909 Издательство: Springer Рейтинг: Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences.
Автор: Rao, Ashwin (stanford University, Usa) Jelvis, Tikhon Название: Foundations of reinforcement learning with applications in finance ISBN: 1032124121 ISBN-13(EAN): 9781032124124 Издательство: Taylor&Francis Рейтинг: Цена: 11482.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book demystifies Reinforcement Learning, and makes it a practically useful tool for those studying and working in applied areas, especially finance. This book seeks to overcome that barrier, and to introduce the foundations of RL in a way that balances depth of understanding with clear, minimally technical delivery.
Автор: Christopher Gatti Название: Design of Experiments for Reinforcement Learning ISBN: 3319385518 ISBN-13(EAN): 9783319385518 Издательство: Springer Рейтинг: Цена: 13415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge.
Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou Название: Deep Reinforcement Learning with Guaranteed Performance ISBN: 3030333868 ISBN-13(EAN): 9783030333867 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Нет в наличии.
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.
Автор: Mohit Sewak Название: Deep Reinforcement Learning ISBN: 9811382875 ISBN-13(EAN): 9789811382871 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Нет в наличии.
Описание: This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications.
Автор: Ravichandiran Sudharsan Название: Hands-On Reinforcement Learning with Python ISBN: 1788836529 ISBN-13(EAN): 9781788836524 Издательство: Неизвестно Цена: 7539.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.
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