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.

Despite blockchain being an emerging technology that is mainly applied in the financial and logistics domain areas, it has great potential to be applied in other industries to generate a wider impact. Due to the need for social distancing globally, blockchain has great opportunities to be adopted in digital health including health insurance, pharmaceutical supply chain, remote diagnosis, and more. Revolutionizing Digital Healthcare Through Blockchain Technology Applications explores the current applications and future opportunities of blockchain technology in digital health and provides a reference for the development of blockchain in digital health for the future. Covering key topics such as privacy, blockchain economy, and cryptocurrency, this reference work is ideal for computer scientists, healthcare professionals, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

* proposes robust time-varying formation control method * discusses multiple uncertainties involving parameter uncertainties, nonlinearities, coupled dynamics, and external disturbances * provides experimental and simulation results are given. * discusses the influence of communication delay and actuator fault

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 book explains the history, current situation, market size and technological level of China's telecommunication industry in detail. It also provides an introduction to the main operators in China and their respective market shares and network technologies. Information about major equipment manufacturing enterprises and their major products is also provided, and their competitive strengths are analyzed. Finally, the book describes the evolution of China's telecommunication regulatory regime, the changes in telecommunication policies and the reform of regulatory practices. The impact of these reform measures is then briefly evaluated.