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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and;

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Networked Control Systems with Intermittent Feedback

Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;

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Networked Control Systems with Intermittent Feedback

Dynamic Programming (ADP) and Reinforcement Learning (RL); and control systems with exogenous disturbances and modeling uncertainties.;

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Algorithms for Reinforcement Learning

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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Algorithms for Reinforcement Learning

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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Approximate Dynamic Programming

of mainstream operations research, artificial intelligence, and control theory, this second edition of Approximate Dynamic Programming Solving the;

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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

This book gives an exposition of recently developed approximate dynamic programming (ADP) techniques for decision and control in human;

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Handbook of Learning and Approximate Dynamic Programming

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

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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Handbook of Reinforcement Learning and Control

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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Reinforcement Learning and Dynamic Programming Using Function Approximators

economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a;

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Frontiers Of Intelligent Control And Information Processing

researchers from adaptive/approximate dynamic programming, reinforcement learning, machine learning, neural optimal control, networked systems, and;

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Human-Robot Interaction Control Using Reinforcemen t Learning

-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for;

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Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author;

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Reinforcement Learning for Finance

library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;

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Deep Reinforcement Learning with Python

, 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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Zai:Deep Reinforcement Learning i_p

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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The The Reinforcement Learning Workshop

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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Simulation-Based Optimization

, Reinforcement Learning and Convergence Analysis - this book is written for researchers and students in the fields of engineering (industrial, systems;

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Hands-On Reinforcement Learning with Python

advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and;

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Self-Learning Optimal Control of Nonlinear Systems

This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which;

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Reinforcement Learning

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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Optimal Networked Control Systems with MATLAB

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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Optimal Networked Control Systems With MATLAB

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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Optimization Under Stochastic Uncertainty

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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Optimization Under Stochastic Uncertainty

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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Motivated Reinforcement Learning

learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The;

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Einde inhoud

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