Liu Derong Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
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Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single
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Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making
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Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a
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Derong Liu Fundamentals of Computational Intelligence
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Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text
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Control Systems and Reinforcement Learning
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H. M. Schwartz Multi-Agent Machine Learning
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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
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Warren B. Powell Reinforcement Learning and Stochastic Optimization
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Buffet Olivier Markov Decision Processes in Artificial Intelligence
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Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It
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Amit Konar Multi-Agent Coordination
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Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with
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Anthonie Wurth Training Reinforcement. The 7 Principles to Create Measurable Behavior Change and Make Learning Stick
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A proven framework to fill the gap between «knowing» and «doing» Training Reinforcement offers expert guidance for more effective training outcomes. Last year, US companies spent over $165 Billon on training; while many training programs themselves provide valuable skills and concepts, even the
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Multi-Agent Reinforcement Learning
Купить за 1490 RUR
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Deep Reinforcement Learning: Academic & Practical Tutorial
Купить за 1400 RUR
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Simon Haykin Nonlinear Filters
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NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging
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