schwartz hm (22 resultados)

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Dust jacket in good condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,450grams, ISBN:0241892880.

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:9780333802632.

Condición: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9780333618523.

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9780230521285.

Hardcover. Condición: Very Good. Estado de la sobrecubierta: Good. Stated First. 208 pages. Includes color plates of fundus (retina?). Former owners name on first blank page. Jacket has 1/2" missing at head of spine. 1" chip at top of front flap fold. Other wear and creasing at edges. Price intact on jacket flap. Jacket protected in mylar.

Hardcover. Condición: 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.

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

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

Condición: new.

Condición: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

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

Condición: New. pp. 350.

Condición: 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. . . . .

Condición: New. pp. 350.

Hardcover. Condición: Brand New. 1st edition. 256 pages. 9.50x6.50x0.75 inches. In Stock.

Gebunden. Condición: 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.

Condición: 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.

Hardcover. Condición: Like New. Like New. book.

Buch. Condición: 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.

Hardcover. Condición: Brand New. 1st edition. 256 pages. 9.50x6.50x0.75 inches. In Stock. This item is printed on demand.

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 513.

Hardcover. Condición: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.