000 | 04725nam a22006255i 4500 | ||
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001 | 978-3-319-78384-0 | ||
003 | DE-He213 | ||
005 | 20210201191329.0 | ||
007 | cr nn 008mamaa | ||
008 | 180510s2018 gw | s |||| 0|eng d | ||
020 |
_a9783319783840 _9978-3-319-78384-0 |
||
050 | 4 | _aTJ212-225 | |
072 | 7 |
_aTJFM _2bicssc |
|
072 | 7 |
_aTEC004000 _2bisacsh |
|
072 | 7 |
_aTJFM _2thema |
|
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 |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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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. |
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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 |