| 000 | 01436nam\a2200277\a\4500 | ||
|---|---|---|---|
| 001 | 57893 | ||
| 005 | 20260707140653.0 | ||
| 008 | 260707s2020 maua b 001 0 eng | ||
| 020 |
_a9780262039246 _qhardcover : alk. paper |
||
| 040 |
_aDLC _beng _cDLC _erda _dBD-DhIUB |
||
| 082 | 0 | 0 |
_a006.31 _223 _bS9671r |
| 100 | 1 |
_aSutton, Richard S., _eauthor. |
|
| 245 | 1 | 0 |
_aReinforcement learning : _ban introduction / _cRichard S. Sutton and Andrew G. Barto. |
| 250 | _aSecond edition. | ||
| 260 |
_aEngland: _bThe MIT Press _c2020 |
||
| 300 |
_axxii, 526 pages : _billustrations (some color) ; _c24 cm. |
||
| 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."-- | ||
| 526 |
_aCSE _bps _lREF |
||
| 541 | _aOmni concept | ||
| 650 | 0 | _aReinforcement learning. | |
| 700 | 1 |
_aBarto, Andrew G., _eauthor. |
|
| 942 |
_2ddc _cBK |
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| 999 |
_c57893 _d58067 |
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