| 000 | 01214nam a22003017a 4500 | ||
|---|---|---|---|
| 005 | 20250922143652.0 | ||
| 008 | 250922b |||||||| |||| 00| 0 eng d | ||
| 020 | _a978-0-262-19398-6 | ||
| 040 | _cSYAMALA BOOK LINKS | ||
| 041 | _aENGLISH | ||
| 082 | _a006.31 SUT | ||
| 100 | _aSUTTON S RICHARD | ||
| 245 |
_aREINFORCEMENT LEARNING _bAN INTRODUCTION |
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| 250 | _aSECOND EDITION | ||
| 260 |
_aLONDON _bTHE MIT PRESS _c2018 |
||
| 300 | _aXIX 526P | ||
| 365 |
_b6450 _c₹ |
||
| 500 | _aOPTIMIZATION & AUTOMATION | ||
| 520 | _a"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto is a fundamental textbook detailing how an agent learns optimal decisions by interacting with an environment to maximize a reward signal. The book covers core concepts such as agents, environments, policies, reward signals, and value functions, and explores methods like Dynamic Programming, Monte Carlo, and Temporal-Difference learning. | ||
| 522 | _aComputer Science | ||
| 526 | _aALL ENGINEERING BRANCHES | ||
| 650 | _aCOMPUTER SCIENCE | ||
| 651 | _aENGINEERING | ||
| 658 | _cR23 | ||
| 700 | _aBARTO G ANDREW | ||
| 760 | _bSECOND EDITION | ||
| 942 |
_2ddc _cREF _eSECOND EDITION _n0 |
||
| 999 |
_c18321 _d18321 |
||