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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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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 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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Linear-Quadratic Controls in Risk-Averse Decision Making

Linear-Quadratic Controls in Risk-Averse Decision Making cuts across control engineering (control feedback and decision optimization) and;

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Synthesis and Operability Strategies for Computer-Aided Modular Process Intensification

developments and real-world applications for computer-aided process modeling, optimization and control, with a particular interest on process;

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Simulation-Based Optimization

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and;

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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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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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Advanced Techniques and Technology of Computer-Aided Feedback Control

This book covers various modern theoretical, technical, practical and technological aspects of computerized numerical control and control;

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Human-Robot Interaction Control Using Reinforcemen t Learning

, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control;

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Towards Integrating Control and Information Theories

This book investigates the performance limitation issues in networked feedback systems. The fact that networked feedback systems consist;

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Learning Control

, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications;

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Advanced Reinforced Concrete Design

indispensable. The book presents innovative optimization aided design methods for concrete structures. Mathematical optimization is applied to;

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Model-Based Control Of Networked Systems

such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time;

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Smart Transportation

Covers the whole impact of AI on smart mobility: new services, fleet intelligence, reinforcement learning, global optimization, new smart;

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Feedback Control Theory

An excellent introduction to feedback control system design, this book offers a theoretical approach that captures the essential issues and;

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Neural Network Control of Nonlinear Discrete-Time Systems

Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels;

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Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles

engineered systems. ADP is a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems. It;

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Computer Aided Design and Design Automation

This volume of The Circuits and Filters Handbook, Third Edition focuses on computer aided design and design automation. In the first part;

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Submodularity in Dynamics and Control of Networked Systems

This book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on;

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Reinforcement Learning Algorithms

is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all;

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Linear Feedback Controls

systems have made feedback controls ubiquitous - a simple cell phone, for example, can have dozens of feedback control systems. Recent research;

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Introduction to Control System Design

system design. Students learn the fundamentals of feedback control systems; stability, regulation, and root locus design; time delay, plant;

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Real Time Optimization by Extremum-seeking Control

. Following the resurgence in popularity of extremum-seeking control in aerospace and automotive engineering, Real-Time Optimization by Extremum;

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Machine Learning for Networking

submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine;

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