04021nam a22005535i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001700167072001700184072002300201072001600224100007400240245019200314250001800506264006900524300006400593336002600657337002600683338003600709347002400745490004700769505056000816520121701376650002502593650002902618650003402647650003202681650002802713650003202741650006102773650003202834650002802866700007502894700007702969700008103046700007903127710003403206773002603240776003603266776003603302776003603338830004703374856004603421978-3-031-45252-9DE-He21320251030111501.0cr nn 008mamaa240305s2024    sz |    s    |||| 0|eng d  a97830314525297 a10.1007/978-3-031-45252-92doi 4aTJ212-225 7aTJFM2bicssc 7aGPFC2bicssc 7aTEC0070002bisacsh 7aTJFM2thema1 aLian, Bosen.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aIntegral and Inverse Reinforcement Learning for Optimal Control Systems and Gamesh[electronic resource] /cby Bosen Lian, Wenqian Xue, Frank L. Lewis, Hamidreza Modares, Bahare Kiumarsi.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXX, 267 p. 43 illus., 41 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aAdvances in Industrial Control,x2193-15770 a1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.  aIntegral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains – aircraft, robotics, power systems, and communication networks among them – with theoretical insights valuable in tackling the real-world challenges they face. 0aControl engineering. 0aEngineering mathematics. 0aEngineeringxData processing. 0aComputational intelligence. 0aAutomotive engineering.14aControl and Systems Theory.24aMathematical and Computational Engineering Applications.24aComputational Intelligence.24aAutomotive Engineering.1 aXue, Wenqian.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aLewis, Frank L.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aModares, Hamidreza.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aKiumarsi, Bahare.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303145251208iPrinted edition:z978303145253608iPrinted edition:z9783031452543 0aAdvances in Industrial Control,x2193-157740uhttps://doi.org/10.1007/978-3-031-45252-9