{"product_id":"9781118884485","title":"Multi-Agent Machine Learning: A Reinforcement Approach","description":"\u003cp\u003eThe 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.\u003cbr\u003e \u003cbr\u003e • Framework for understanding a variety of methods and approaches in multi-agent machine learning.\u003cbr\u003e \u003cbr\u003e • Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning\u003cbr\u003e \u003cbr\u003e • Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47107288301808,"sku":"9781118884485","price":114.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781118884485_p0.jpg?v=1769889774","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781118884485","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}