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Condition: New. pp. 312 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
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Condition: New. pp. 312.
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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: New. pp. 312.
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Ultimate Genetic Algorithms with Python: Build Intelligent and Adaptive AI Systems with Genetic Algorithms in Python for Machine Learning, Deep Learning, and Multi-Agent Domains (English Edition)
Language: English
Published by Orange Education Pvt Ltd, 2025
ISBN 10: 9349888335 ISBN 13: 9789349888333
Condition: New.
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Ultimate Genetic Algorithms with Python: Build Intelligent and Adaptive AI Systems with Genetic Algorithms in Python for Machine Learning, Deep Learning, and Multi-Agent Domains (English Edition)
Language: English
Published by Orange Education Pvt Ltd, 2025
ISBN 10: 9349888335 ISBN 13: 9789349888333
Condition: New.
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Condition: Gut. 1997. 312 Seiten Anderes Cover. Von 1997. Innerhalb Deutschlands Versand je nach Größe/Gewicht als Großbrief bzw. Bücher- und Warensendung mit der Post oder per DHL. Rechnung mit MwSt.-Ausweis liegt jeder Lieferung bei. Sprache: Englisch Gewicht in Gramm: 408 Taschenbuch, Größe: 15.5 x 1.8 x 23.5 cm.
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Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments: ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, ... (Lecture Notes in Computer Science, 1221)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments: ECAI?96 Workshop LDAIS, Budapest, Hungary, August 13,
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Condition: New. 1997. 1997th Edition. paperback. . . . . .
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Condition: as new. Livraison rapide, bien emballé, service client soigné.Pour tout renseignement complémentaire, n'hésitez pas à nous contacter.
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Condition: As New. Unread book in perfect condition.
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Condition: New. 1997. 1997th Edition. paperback. . . . . . Books ship from the US and Ireland.
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Condition: New. pp. 350.
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Condition: New.
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Multi-Agent Machine Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Add to basketHRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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Condition: New. pp. 350.
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Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments : ECAI'96 Workshop LDAIS, Budapest, Hungary, August 13, 1996, ICMAS'96 Workshop LIOME, Kyoto, Japan, December 10, 1996 Selected Papers
Language: English
Published by Springer Berlin Heidelberg, 1997
ISBN 10: 3540629343 ISBN 13: 9783540629344
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.
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Multi-Agent Machine Learning : A Reinforcement Approach
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Multi-Agent Machine Learning (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2014
ISBN 10: 111836208X ISBN 13: 9781118362082
First Edition
Hardcover. Condition: new. Hardcover. 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 gamestwo 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Multi-Agent Machine Learning : A Reinforcement Approach
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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
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Multi-Agent Machine Learning: A Reinforcement Approach
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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First Edition
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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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Multi-Agent Machine Learning (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2014
ISBN 10: 111836208X ISBN 13: 9781118362082
First Edition
US$ 135.88
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Add to basketHardcover. Condition: new. Hardcover. 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 gamestwo 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 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Add to basketGebunden. Condition: New. Howard M. Schwartz, PhD, received his B.Eng. Degree from McGill University, Montreal, Canada in une 1981 and his MS Degree and PhD Degree from MIT, Cambridge, USA in 1982 and 1987 respectively. He is currently a professor in systems and computer engineering.
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