9781118362082-111836208X-Multi-Agent Machine Learning: A Reinforcement Approach

Multi-Agent Machine Learning: A Reinforcement Approach

ISBN-13: 9781118362082
ISBN-10: 111836208X
Edition: 1
Author: Schwartz, H. M.
Publication date: 2014
Publisher: Wiley
Format: Hardcover 256 pages
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Book details

ISBN-13: 9781118362082
ISBN-10: 111836208X
Edition: 1
Author: Schwartz, H. M.
Publication date: 2014
Publisher: Wiley
Format: Hardcover 256 pages

Summary

Acknowledged authors Schwartz, H. M. wrote Multi-Agent Machine Learning: A Reinforcement Approach comprising 256 pages back in 2014. Textbook and eTextbook are published under ISBN 111836208X and 9781118362082. Since then Multi-Agent Machine Learning: A Reinforcement Approach textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

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. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games―two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits.

• Framework for understanding a variety of methods and approaches in multi-agent machine learning.

• Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning

• Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering

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