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Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;
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Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;
Vergelijkbare producten zoals Networked Control Systems with Intermittent Feedback
Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;
Vergelijkbare producten zoals Networked Control Systems with Intermittent Feedback
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure;
Vergelijkbare producten zoals Algorithms for Reinforcement Learning
of mainstream operations research, artificial intelligence, and control theory, this second edition of Approximate Dynamic Programming Solving the;
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This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human;
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to start implementing the learning algorithms provided in the book* Includes ideas, directions, and recent results on current research issues;
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Dynamic Programming for Impulse Feedback and Fast Controls offers a description of feedback control in the class of impulsive inputs. This;
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This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory;
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economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a;
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researchers from adaptive/approximate dynamic programming, reinforcement learning, machine learning, neural optimal control, networked systems, and;
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-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for;
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performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author;
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library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;
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, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding;
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of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously;
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Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and;
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, Reinforcement Learning and Convergence Analysis - this book is written for researchers and students in the fields of engineering (industrial, systems;
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advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and;
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This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which;
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Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their;
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in discrete time for linear and nonlinear systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for finite;
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in discrete time for linear and nonlinear systems. It lays the foundation for reinforcement learning-based optimal adaptive controller use for finite;
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described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls;
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described, as open-loop control, closed-loop, feedback control and open-loop feedback control, used for iterative construction of feedback controls;
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learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The;
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