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020 _a9783319783840
_9978-3-319-78384-0
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aTEC004000
_2bisacsh
072 7 _aTJFM
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082 0 4 _a629.8
_223
100 1 _aKamalapurkar, Rushikesh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aReinforcement Learning for Optimal Feedback Control
_h[electronic resource] :
_bA Lyapunov-Based Approach /
_cby Rushikesh Kamalapurkar, Patrick Walters, Joel Rosenfeld, Warren Dixon.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXVI, 293 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aCommunications and Control Engineering,
_x0178-5354
500 _aAcceso multiusuario
505 0 _aChapter 1. Optimal control -- Chapter 2. Approximate dynamic programming -- Chapter 3. Excitation-based online approximate optimal control -- Chapter 4. Model-based reinforcement learning for approximate optimal control -- Chapter 5. Differential Graphical Games -- Chapter 6. Applications -- Chapter 7. Computational considerations -- Reference -- Index.
520 _aReinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book's focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor-critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aControl engineering.
650 0 _aCalculus of variations.
650 0 _aSystem theory.
650 0 _aElectrical engineering.
650 1 4 _aControl and Systems Theory.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T19010
650 2 4 _aCalculus of Variations and Optimal Control; Optimization.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M26016
650 2 4 _aSystems Theory, Control.
_0https://scigraph.springernature.com/ontologies/product-market-codes/M13070
650 2 4 _aCommunications Engineering, Networks.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T24035
700 1 _aWalters, Patrick.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aRosenfeld, Joel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aDixon, Warren.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319783833
776 0 8 _iPrinted edition:
_z9783319783857
776 0 8 _iPrinted edition:
_z9783030086893
830 0 _aCommunications and Control Engineering,
_x0178-5354
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-78384-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c242232
_d242231