Multi-Agent Machine Learning: A Reinforcement Approach (eng)
Language: English
Published by Wiley, 2014
- Hardcover
- New
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- Title
- Multi-Agent Machine Learning: A Reinforcement Approach (eng)
- Author
- Schwartz, H. M.
- Publisher
- Wiley
- Publication year
- 2014
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 111836208X
- ISBN 13
- 9781118362082
- Item weight
- 0.52 kilograms
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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Brook Bookstore On Demand
Napoli, NA, Italy
AbeBooks seller since October 11, 2022
Shipping rates from Italy to U.S.A.
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