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Multi Agent Machine Learning Reinforcement by Schwartz (13 results)
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Hardcover. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Condition: new.
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Condition: New.
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Multi-Agent Machine Learning : A Reinforcement Approach
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 117.83
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New.
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Multi-Agent Machine Learning : A Reinforcement Approach
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 130.66
US$ 20.11 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Multi-Agent Machine Learning: A Reinforcement Approach
Language: English
Published by John Wiley & Sons Inc, 2014
ISBN 10: 111836208X ISBN 13: 9781118362082
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
US$ 139.79
US$ 21.29 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days.
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Multi-Agent Machine Learning: A Reinforcement Approach
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
Seller rating 5 out of 5 stars
First Edition
US$ 162.09
US$ 12.20 shipping
Ships from Ireland to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. 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. Num Pages: 256 pages, illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 163 x 238 x 18. Weight in Grams: 478. . 2014. 1st Edition. Hardcover. . . . .
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Condition: New. 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. Num Pages: 256 pages, illustrations. BIC Classification: UYQM. Category: (P) Professional & Vocational. Dimension: 163 x 238 x 18. Weight in Grams: 478. . 2014. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.
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US$ 203.03
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Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: Like New. Like New. book.
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Buch. Condition: Neu. Neuware - 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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Multi-Agent Machine Learning: A Reinforcement Approach
Language: English
Published by John Wiley & Sons Inc, 2014
ISBN 10: 111836208X ISBN 13: 9781118362082
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
US$ 170.60
US$ 21.29 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 513.
