Machine learning multi agent (55 résultats)

Etat : New.

Etat : 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.…

Etat : New.

Hardcover. Etat : Good. Moderate wear. A small portion of pages have some underlining and /or margin notes - less than 4 % of the pages of the book (approximately 16 pages and mostly at the beginning of the book) . Also, there is writing on the front cover page. Satisfaction guaranteed with 30-day return policy. Ships from Canada.; 157.5 X 17.8 X 238.8 millimeters; 256 pages; R0 3.2m/2.7m s0.…

PF. Etat : New.

Etat : New. In English.

Etat : as new. Livraison rapide, bien emballé, service client soigné.Pour tout renseignement complémentaire, n'hésitez pas à nous contacter.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : Very Good. 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. …

Taschenbuch. Etat : 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.…

Hardback. Etat : Very Good. 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. …

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : new.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Etat : New. pp. 350.

Etat : New. In English.

Etat : 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. . . . . …

Etat : New. pp. 350.

Paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Etat : 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.

Etat : 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. …

Etat : New.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Etat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Buch. Etat : 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. …