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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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Multi-Agent Machine Learning

methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games;

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Reinforcement Learning

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational;

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Differential Games Of Pursuit

of differential games and are rigorously investigated. In this book, the full theory of differential games of pursuit with complete and partial;

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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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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together;

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Deep Reinforcement Learning with Guaranteed Performance

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding;

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Nonlinear Optimal Control Theory

governed by ordinary differential equations and certain types of differential equations with memory. Many examples illustrate the mathematical;

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Motivated Reinforcement Learning

develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement;

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Stability, Control and Differential Games

of Sciences. Discussing the latest advances in the theory of optimal control, stability theory and differential games, it also demonstrates the;

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Stability, Control and Differential Games

of Sciences. Discussing the latest advances in the theory of optimal control, stability theory and differential games, it also demonstrates the;

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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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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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Hands-On Reinforcement Learning for Games

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and;

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Trends in Nonlinear and Adaptive Control

-input-multiple-output nonlinear systems; input delays; partial differential equations; population games; and data-driven control;

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Reinforcement Learning From Scratch

acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Koelling, King's College London) and;

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Learning-Based Adaptive Control

demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and;

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Stochastic Linear-Quadratic Optimal Control Theory: Differential Games and Mean-Field Problems

important aspect of stochastic control. It presents results for two-player differential games and mean-field optimal control problems in the context;

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Optimal Control

, the fundamentals of optimal control theory. It covers the major topics involving measurement, principles of optimality, dynamic programming;

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Learning to Play

of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation;

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Hamilton-Jacobi-Bellman Equations

controllers and the control of nonlinear delay differential equations. Contents From a monotone probabilistic scheme to a probabilistic;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Controlled Markov Processes and Viscosity Solutions

This book is an introduction to optimal stochastic control for continuous time Markov processes and the theory of viscosity solutions. It;

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Control Theory and Dynamic Games in Economic Policy Analysis

thoroughly discussed. The tools of optimal control and differential game theory, and their advantages and drawbacks, are examined, along with;

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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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Partially Observed Markov Decision Proce

of stochastic dynamic programming, lattice programming and reinforcement learning for POMDPs. Questions addressed in the book include: when does a POMDP;

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