{ "item_title" : "Multi-Agent Machine Learning", "item_author" : [" H. M. Schwartz "], "item_description" : "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", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/1/11/836/208/111836208X_b.jpg", "price_data" : { "retail_price" : "123.95", "online_price" : "123.95", "our_price" : "123.95", "club_price" : "123.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

Overview

Customers Also Bought

Details