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Multi-Agent Machine Learning: A Reinforcement Approach 1st Edition

by H. M. Schwartz (Author)

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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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ISBN-10

9781118362082
ISBN-13

978-1118362082
Edition

1st
Publisher

Wiley
Publication date

August 11, 2014
Language

English
Dimensions

6.2 x 0.7 x 9.4 inches
Print length

256 pages

See all details

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Editorial Reviews

Review

“This is an interesting book both as research reference as well as teaching material for Master and PhD students.” (Zentralblatt MATH, 1 April 2015)

From the Inside Flap

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.

ASIN ‏ : ‎ 111836208X
Publisher ‏ : ‎ Wiley
Publication date ‏ : ‎ August 11, 2014
Edition ‏ : ‎ 1st
Language ‏ : ‎ English
Print length ‏ : ‎ 256 pages
ISBN-10 ‏ : ‎ 9781118362082
ISBN-13 ‏ : ‎ 978-1118362082
Item Weight ‏ : ‎ 1.06 pounds
Dimensions ‏ : ‎ 6.2 x 0.7 x 9.4 inches
Best Sellers Rank: #4,093,630 in Books (See Top 100 in Books)
- #486 in Game Theory (Books)
- #9,921 in Electrical & Electronics (Books)
- #13,525 in AI & Machine Learning
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