Handbook of Reinforcement Learning and Control
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Beschreibung
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.
Enriches understanding of the applications of reinforcement learning for control of dynamic systems Collates research from a wide-range of experts, creating a comprehensive guide Discusses both theoretical and practical aspects of machine learning and control
Autor*in
Kyriakos G. Vamvoudakis
Themen in »Handbook of Reinforcement Learning and Control«
Reinforcement Learning Privacy and Security Control Systems Distributed Control Multi-Agent Systems Game Theory Non-equilibrium Learning Optimal and Adaptive Control Data-driven Decision Making