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.
General
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