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|a 0-262-19398-1
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| 040 |
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|a AR-CdUBP
|b spa
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| 041 |
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|a eng
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| 100 |
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|a Sutton, Richard S.
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| 245 |
1 |
0 |
|a Reinforcement learning :
|b an introduction /
|c Richard S. Sutton, Andrew G. Barto
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| 260 |
|
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|a Cambridge, Massachusetts :
|b MIT,
|c c1998
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| 300 |
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|a xi, 322 p. ;
|c 20 cm.
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| 490 |
0 |
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|a Adaptive computation and machine learning
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| 502 |
|
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|a Bibliografía: p. 291-312
|
| 505 |
0 |
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|a I. The problem. 1. Introduction. 2. Evaluative feedback. 3. The reinforcement learning problem. II. Elementary solution methods. 4. Dynamic programming. 5. Monte Carlo Methods. 6. Temporal-difference learning. III. A unified view. 7. Eligibility traces. 8. Generalization and function approximation. 9. Planning and learning. 10. Dimensions of reinforcement learning. 11. Case studies.
|
| 650 |
|
4 |
|a INTELIGENCIA ARTIFICIAL
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| 653 |
|
|
|a INFORMATICA
|
| 700 |
1 |
|
|a Barto, Andrew G.
|
| 930 |
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|a INFORMATICA
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| 931 |
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|a 01843
|b UBP
|
| 942 |
|
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|2 cdu
|c BK
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| 945 |
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|a SMM
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| 984 |
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|a 004.85
|b Su87
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| 999 |
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|c 17458
|d 17458
|