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001			978-3-031-28394-9
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008			230724s2023 sz | s |||| 0|eng d
020			_a9783031283949 _9978-3-031-28394-9
050		4	_aTJ212-225
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100	1		_aLi, Jinna. _eauthor. _0(orcid)0000-0001-9985-6308 _1https://orcid.org/0000-0001-9985-6308 _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aReinforcement Learning _h[electronic resource] : _bOptimal Feedback Control with Industrial Applications / _cby Jinna Li, Frank L. Lewis, Jialu Fan.
250			_a1st ed. 2023.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023.
300			_aXVI, 310 p. 114 illus., 110 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Industrial Control, _x2193-1577
500			_aAcceso multiusuario
505	0		_a1. Background on Reinforcement Learning and Optimal Control -- 2. H-infinity Control Using Reinforcement Learning -- 3. Robust Tracking Control and Output Regulation -- 4. Interleaved Robust Reinforcement Learning -- 5. Optimal Networked Controller and Observer Design -- 6. Interleaved Q-Learning -- 7. Off-Policy Game Reinforcement Learning -- 8. Game Reinforcement Learning for Process Industries.
520			_aThis book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
541			_fUABC ; _cPerpetuidad
650		0	_aControl engineering.
650		0	_aComputational intelligence.
650		0	_aProduction engineering.
650		0	_aEngineering mathematics.
650		0	_aEngineering _xData processing.
650		0	_aIndustrial engineering.
650		0	_aSystem theory.
650	1	4	_aControl and Systems Theory.
650	2	4	_aComputational Intelligence.
650	2	4	_aProcess Engineering.
650	2	4	_aMathematical and Computational Engineering Applications.
650	2	4	_aIndustrial and Production Engineering.
650	2	4	_aComplex Systems.
700	1		_aLewis, Frank L. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aFan, Jialu. _eauthor. _0(orcid)0000-0001-7585-1166 _1https://orcid.org/0000-0001-7585-1166 _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031283932
776	0	8	_iPrinted edition: _z9783031283956
776	0	8	_iPrinted edition: _z9783031283963
830		0	_aAdvances in Industrial Control, _x2193-1577
856	4	0	_zLibro electrónico _uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-28394-9
912			_aZDB-2-INR
912			_aZDB-2-SXIT
942			_cLIBRO_ELEC
999			_c262285 _d262284