000 02214nam a22002297a 4500
005 20261025114230.0
008 261025b |||||||| |||| 00| 0 eng d
020 _a9780262039246
028 _a: International Book Distributors
_b: Flat No. 17, Prakash Apartment, Ansari Road, Daryaganj, New Delhi.
_c: 123255
_d: 21/10/2026
082 _a006.31 SUT
100 _aSutton, Richard S.
100 _a; Barto, Andrew G.
245 _aReinforcement Learning
_b: An Introduction.
_c/ Richard S Sutton.
260 _aCambridge
_b: MIT Press
_c, 2018.
300 _axxii, 526p.
_c: 24cm.
365 _b8001
520 _aReinforcement 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.
650 _aReinforcement Learning
650 _aMachine learning
700 _aRichard S Sutton.
942 _cBOOK
999 _c70926
_d70926