Reinforcement learning : an introduction
Richard S. Sutton and Andrew G. Barto
Bok Engelsk 2018
| Medvirkende | |
|---|---|
| Omfang | XXII, 526 sider : figurer
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| Utgave | Second edition
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| Opplysninger | Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby and 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 topic and updating coverage of other topics. Like the first edition, this new 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 II has new chapters on reinforcement learning's relationship with psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning
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| Emner | |
| Dewey | |
| ISBN | 9780262039246
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