LEADER 01668nas 2200505- 450 001 996336058603316 005 20240113213019.0 011 $a1855-6531 035 $a(OCoLC)656541375 035 $a(CKB)2670000000046593 035 $a(CONSER)--2021244872 035 $a(EXLCZ)992670000000046593 100 $a20100303a20069999 --- - 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in production engineering & management 210 1$aMaribor, Slovenia :$cFaculty of Mechanical Engineering, University of Maribor,$d[2006]- 215 $a1 online resource 300 $aRefereed/Peer-reviewed 311 $a1854-6250 517 1 $aAdvances in production engineering and management 517 1 $aAPEM 606 $aProduction engineering$vPeriodicals 606 $aProduction engineering$xManagement$vPeriodicals 606 $aTechnique de la production$vPériodiques 606 $aGestion$vPériodiques 606 $aManagement$2fast$3(OCoLC)fst01007141 606 $aProduction engineering$2fast$3(OCoLC)fst01078282 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aProduction engineering 615 0$aProduction engineering$xManagement 615 6$aTechnique de la production 615 6$aGestion 615 7$aManagement. 615 7$aProduction engineering. 676 $a[E] 712 02$aUniverza v Mariboru.$bFakulteta za strojni?tvo, 906 $aJOURNAL 912 $a996336058603316 996 $aAdvances in production engineering & management$92195839 997 $aUNISA LEADER 04630nam 22007695 450 001 9910735784103321 005 20230724231851.0 010 $a3-031-28394-5 024 7 $a10.1007/978-3-031-28394-9 035 $a(MiAaPQ)EBC30663112 035 $a(Au-PeEL)EBL30663112 035 $a(DE-He213)978-3-031-28394-9 035 $a(PPN)272250686 035 $a(CKB)27857483900041 035 $a(EXLCZ)9927857483900041 100 $a20230724d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement Learning $eOptimal Feedback Control with Industrial Applications /$fby Jinna Li, Frank L. Lewis, Jialu Fan 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (318 pages) 225 1 $aAdvances in Industrial Control,$x2193-1577 311 08$aPrint version: Li, Jinna Reinforcement Learning Cham : Springer International Publishing AG,c2023 9783031283932 327 $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. 330 $aThis book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries. 410 0$aAdvances in Industrial Control,$x2193-1577 606 $aAutomatic control 606 $aComputational intelligence 606 $aProduction engineering 606 $aEngineering mathematics 606 $aEngineering?Data processing 606 $aIndustrial engineering 606 $aSystem theory 606 $aControl and Systems Theory 606 $aComputational Intelligence 606 $aProcess Engineering 606 $aMathematical and Computational Engineering Applications 606 $aIndustrial and Production Engineering 606 $aComplex Systems 615 0$aAutomatic control. 615 0$aComputational intelligence. 615 0$aProduction engineering. 615 0$aEngineering mathematics. 615 0$aEngineering?Data processing. 615 0$aIndustrial engineering. 615 0$aSystem theory. 615 14$aControl and Systems Theory. 615 24$aComputational Intelligence. 615 24$aProcess Engineering. 615 24$aMathematical and Computational Engineering Applications. 615 24$aIndustrial and Production Engineering. 615 24$aComplex Systems. 676 $a006.31 700 $aLi$b Jinna$01379090 701 $aLewis$b Frank L$030830 701 $aFan$b Jialu$01379091 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910735784103321 996 $aReinforcement Learning$93418559 997 $aUNINA