Reinforcement learning : an introduction
Author(s)
Bibliographic Information
Reinforcement learning : an introduction
(Adaptive computation and machine learning)
MIT Press, c2018
2nd ed
Available at / 89 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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University of Tsukuba Library, Library on Library and Information Science
007.1-Su8410018017134
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Etchujima library, Tokyo University of Marine Science and Technology工流通情報システム
007.1/Su84202151293
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Institute of Materials and Systems for Sustainability, Nagoya University未来材料研
007.13||Su41692194
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Note
Bibliographical references: p. [481]-518
Index: p. [519]-524
Description and Table of Contents
Description
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.
by "Nielsen BookData"