Multi Agent Machine Learning Reinforcement by Schwartz (15 results)

Hardcover. Condition: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

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: New.

Condition: new.

Condition: As New. Unread book in perfect condition.

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. 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. 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. . . . . Books ship from the US and Ireland.…

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.…

Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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