Handbook of Reinforcement Learning and Control: 325 (Studies in Systems, Decision and Control, 325)
Kyriakos G. Vamvoudakis (editor), Yan Wan (editor), Frank L. Lewis (editor), Derya Cansever (editor)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.
Categorías:
Volumen:
325
Año:
2021
Edición:
1st ed. 2021
Editorial:
Springer
Idioma:
english
Páginas:
857
ISBN 10:
3030609898
ISBN 13:
9783030609894
Serie:
Studies in Systems, Decision and Control
Archivo:
PDF, 20.10 MB
IPFS:
,
english, 2021