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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 of dynamic systems and future directions for related
research and technology. The contributions gathered in this book
deal with challenges faced when using learning and adaptation
methods to solve academic and industrial problems, such as
optimization in dynamic environments with single and multiple
agents, convergence and performance analysis, and online
implementation. They explore means by which these difficulties can
be solved, and cover a wide range of related topics including: deep
learning; artificial intelligence; applications of game theory;
mixed modality learning; and multi-agent reinforcement learning.
Practicing engineers and scholars in the field of machine learning,
game theory, and autonomous control will find the Handbook of
Reinforcement Learning and Control to be thought-provoking,
instructive and informative.
This handbook presents state-of-the-art research in reinforcement
learning, focusing on its applications in the control and game
theory of dynamic systems and future directions for related
research and technology. The contributions gathered in this book
deal with challenges faced when using learning and adaptation
methods to solve academic and industrial problems, such as
optimization in dynamic environments with single and multiple
agents, convergence and performance analysis, and online
implementation. They explore means by which these difficulties can
be solved, and cover a wide range of related topics including: deep
learning; artificial intelligence; applications of game theory;
mixed modality learning; and multi-agent reinforcement learning.
Practicing engineers and scholars in the field of machine learning,
game theory, and autonomous control will find the Handbook of
Reinforcement Learning and Control to be thought-provoking,
instructive and informative.
This monograph describes the use of principles of reinforcement
learning (RL) to design feedback policies for continuous-time
dynamical systems that combine features of adaptive control and
optimal control. In a control engineering context, RL bridges the
gap between traditional optimal control and adaptive control
algorithms.The authors give an insightful introduction to
reinforcement learning techniques that can address various control
problems. In this context, they give a detailed description of
techniques such as Game-Theoretic Learning, Q-learning, and
Intermittent RL; with each chapter providing a self-contained
exposition of the topic and giving the reader suggestions for
further reading. Finally, the authors demonstrate the application
of the techniques in autonomous vehicles.This review of a topic
that is rapidly becoming ubiquitous in many engineering systems
enables to reader dip in and out of the topic to quickly understand
the essentials and provides the starting point for further
research.
Adaptive controllers and optimal controllers are two distinct
methods for the design of automatic control systems. Adaptive
controllers learn online in real time how to control systems but do
not yield optimal performance, whereas optimal controllers must be
designed offline using full knowledge of the systems dynamics. This
book shows how approximate dynamic programming a reinforcement
machine learning technique that is motivated by learning mechanisms
in biological and animal systems can be used to design a family of
adaptive optimal control algorithms that converge in realtime to
optimal control solutions by measuring data along the system
trajectories. The book also describes how to use approximate
dynamic programming methods to solve multiplayer differential games
online. Differential games have been shown to be important in
Hinfinity robust control for disturbance rejection, and in
coordinating activities among multiple agents in networked teams.
The focus of this book is on continuoustime systems, whose
dynamical models can be derived directly from physical principles
based on Hamiltonian or Lagrangian dynamics. Simulation examples
are given throughout the book, and several methods are described
that do not require full state dynamics information. Optimal
Adaptive Control and Differential Games by Reinforcement Learning
Principles is an essential addition to the bookshelves of
mechanical, electrical, and aerospace engineers working in feedback
control systems design."
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