Multi Agent Machine Learning Reinforcement by Schwartz (14 results)

Hardcover. Condition: 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.…

Condition: As New. Unread book in perfect condition.

Hardback. Condition: 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.…

Hardback. Condition: 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.…

Condition: New.

Condition: new.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

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

Condition: New. In English.

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. . . . .…

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.…

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