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040			_aNISER LIBRARY _beng _cNISER LIBRARY
041			_aEnglish
082			_a004.8 _bSUT-R
100			_aSutton, Richard S.
245			_aReinforcement learning : _ban introduction
250			_a2nd ed.
260			_aCambridge, Massachusetts : _bThe MIT Press, _c2018
300			_axxii, 526 p. : _billustrations (some color) ; _c24 cm
490			_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references (pages 481-518) and index.
520			_aThe 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.
650			_aReinforcement learning
700			_aBarto, Andrew G.
856			_3Table of Contents _uhttps://mitp-content-server.mit.edu/books/content/sectbyfn/books_pres_0/10094/Toc.pdf?dl=1
856			_3Reviews _uhttps://www.goodreads.com/book/show/39813875-reinforcement-learning?ref=nav_sb_ss_1_13#CommunityReviews
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