Reinforcement Learning: An Introduction, 2/e (Hardcover)
Richard S. Sutton, Andrew G. Barto
- 出版商: A Bradford Book
- 出版日期: 2018-11-13
- 售價: $1,750
- 貴賓價: 9.8 折 $1,715
- 語言: 英文
- 頁數: 552
- 裝訂: Hardcover
- ISBN: 0262039249
- ISBN-13: 9780262039246
-
相關分類:
Reinforcement 強化學習、Machine Learning 機器學習 、DeepLearning 深度學習
-
相關翻譯:
強化學習, 2/e (Reinforcement Learning: An Introduction, 2/e) (簡中版)
銷售排行:
🥈 2018/11 英文書 銷售排行 第 2 名
立即出貨
買這商品的人也買了...
-
$3,300$3,135 -
$3,530$3,354 -
$1,780$1,744 -
$1,650$1,617 -
$1,200$1,140 -
$2,470$2,347 -
$1,650$1,617 -
$1,485$1,411 -
$857深度學習 (Deep Learning)
-
$450$356 -
$440$374 -
$1,150$1,093 -
$179人工智能基礎 (高中版)
-
$798Deep Reinforcement Learning Hands-On
-
$2,498Natural Language Processing with PyTorch
-
$1,470$1,397 -
$4,230$4,019 -
$520$364 -
$1,008$958 -
$1,300$1,235 -
$690$455 -
$2,230$2,119 -
$980$833 -
$352代碼精進之路 從碼農到工匠
-
$1,400$1,330
相關主題
商品描述
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
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. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.