Springer Handbook of Reinforcement Learning and Control: 325 (Studies in Systems, Decision and Control, 325)

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.  From the Back Cover 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.  About the Author Kyriakos G. Vamvoudakis serves as an Assistant Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He received the Diploma in Electronic and Computer Engineering from the Technical University of Crete, Greece in 2006. He received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively from the University of Texas, Arlington. During the period from 2012 to 2016 he was a project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. His research interests include reinforcement learning, control theory, and safe/assured autonomy. He is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, and of several international awards including the 2016 International Neural Network Society Young Investigator Award. He currently is an Associate Editor of: Automatica; IEEE Computational Intelligence Magazine; IEEE Transactions on Systems, Man, and Cybernetics: Systems; Neurocomputing; Journal of Optimization Theory and Applications; and of IEEE Control Systems Letters. Yan Wan is currently an Associate Professor in the Electrical Engineering Department at the University of Texas at Arlington. She received her Ph.D. degree in Electrical Engineering from Washington State University in 2009 and then did postdoctoral training at the University of California, Santa Barbara. Her research interests lie in the modeling, evaluation, and control of large-scale dynamical networks, cyber-physical system and stochastic networks. She has been recognized by several prestigious awards, including the NSF CAREER Award, RTCA William E. Jackson Award and U.S. Ignite and GENI demonstration awards. She currently serves as the Associate Editor for IEEE Transactions on Control of Network Systems,