TY  - DATA
AU  - Vamvoudakis, G. K.
AU  - Cansever, D.
AU  - Lewis, F.
AU  - Wan, Y.
TI  - Handbook of Reinforcement Learning and Control
SN  - 9783030609900
U1  - 629.836 
PY  - 2021///
CY  - Cham
PB  - : Springer International Publishing
KW  - Automatic Control-Sensitivity
KW  - Reinforcement Learning
N2  - 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
UR  - https://ebookcentral.proquest.com/lib/iitjin/detail.action?docID=6676404&query=Handbook+of+Reinforcement+Learning+and+Control
ER  -