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020			_a9783030609900
024	7		_a10.1007/978-3-030-60990-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.6 _b2021 EB
245	1	0	_aHandbook of Reinforcement Learning and Control _cedited by Kyriakos G. Vamvoudakis, Yan Wan, Frank L. Lewis, Derya Cansever.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XXIV, 833 páginas) _b159 ilustraciones, 145 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aStudies in Systems, Decision and Control _x2198-4190 _v325
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aThe Cognitive Dialogue: A New Architecture for Perception and Cognition -- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems -- Quantum Reinforcement Learning in Changing Environment -- The Role of Thermodynamics in the Future Research Directions in Control and Learning -- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming -- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning -- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach -- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay -- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast -- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning -- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games -- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles -- Long-Term Impacts of Fair Machine Learning -- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization -- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization -- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics -- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning -- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications -- Reinforcement Learning Applications, An Industrial Perspective -- A Hybrid Dynamical Systems Perspective of Reinforcement Learning -- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras -- Mixed Modality Learning -- Computational Intelligence in Uncertainty Quantification for Learning Control and Games -- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback -- Robust Autonomous Driving with Humans in the Loop -- Boundedly Rational Reinforcement Learning for Secure Control.
520	3		_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.
988			_aSpringer_Robotics_2021
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aVamvoudakis, Kyriakos G. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682640
700			_aWang, Yan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _995748
700	1		_aLewis, Frank L. _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _996329
700			_aCansever, Derya _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682641
776	0	8	_iPrinted edition: _z9783030609894
776	0	8	_iPrinted edition: _z9783030609917
776	0	8	_iPrinted edition: _z9783030609924
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-60990-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE