000 | 01685cam a2200349 i 4500 | ||
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001 | 20515853 | ||
003 | OSt | ||
005 | 20221226065029.0 | ||
008 | 180525s2018 maua b 001 0 eng | ||
010 | _a 2018023826 | ||
020 | _a9780262039246 (hardcover : alk. paper) | ||
040 |
_aDLC _beng _cDLC _erda _dDLC |
||
042 | _apcc | ||
050 | 0 | 0 |
_aQ325.6 _b.R45 2018 |
082 | 0 | 0 |
_a006.31 _222 |
100 | 1 |
_aSutton, Richard S., _eauthor. |
|
245 | 1 | 0 |
_aReinforcement learning: an introduction _cRichard S. Sutton and Andrew G. Barto. |
250 | _aSecond edition. | ||
260 |
_aCambridge, Massachusetts : _bThe MIT Press, _c[2018] |
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300 |
_axxii, 526 pages : _billustrations (some color) ; _c24 cm. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_aunmediated _bn _2rdamedia |
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338 |
_avolume _bnc _2rdacarrier |
||
490 | 0 | _aAdaptive computation and machine learning series | |
504 | _aIncludes bibliographical references (pages 481-518) and index. | ||
520 |
_a"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."-- _cProvided by publisher. |
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650 | 0 | _aReinforcement learning. | |
700 | 1 |
_aBarto, Andrew G., _eauthor. |
|
906 |
_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg |
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942 |
_2ddc _cBK _n0 |
||
999 |
_c30674 _d30674 |