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."