000			01946nmm a2200229Ia 4500
008			220920s9999||||xx |||||||||||||| ||und||
020			_a9783030609900
082			_a629.836 _bV259H
100			_aVamvoudakis, G. K. _eAuthor _lEnglish _92589
245		0	_aHandbook of Reinforcement Learning and Control _c/ edited by G. K. Vamvoudakis and others. _h[Electronic Resource]
260			_aCham _b: Springer International Publishing, _c2021
300			_a839p.
440			_aStudies in Systems, Decision and Control Series _vVol. 325 _92590
520			_aThis 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.
650			_aAutomatic Control-Sensitivity _92591
650			_aReinforcement Learning _915983
700			_aCansever, D. _i[Editor] _92592
700			_aLewis, F. _i[Author] _92593
700			_aWan, Y. _i[Author] _92155
856			_uhttps://ebookcentral.proquest.com/lib/iitjin/detail.action?docID=6676404&query=Handbook+of+Reinforcement+Learning+and+Control _qPDF _yClick to Access the Online Book
942			_cEBK _nYes
999			_c12496 _d12496