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Reinforcement Learning

- An Introduction
Af: Richard S. Sutton, Andrew G. Barto Engelsk Hardback

Reinforcement Learning

- An Introduction
Af: Richard S. Sutton, Andrew G. Barto Engelsk Hardback
Tjek vores konkurrenters priser
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.

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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.

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Produktdetaljer
Sprog: Engelsk
Sider: 552
ISBN-13: 9780262039246
Indbinding: Hardback
Udgave:
ISBN-10: 0262039249
Kategori: Machine learning
Udg. Dato: 13 nov 2018
Længde: 33mm
Bredde: 236mm
Højde: 187mm
Forlag: MIT Press Ltd
Oplagsdato: 13 nov 2018
Forfatter(e): Richard S. Sutton, Andrew G. Barto
Forfatter(e) Richard S. Sutton, Andrew G. Barto

Kategori Machine learning

ISBN-13 9780262039246

Sprog Engelsk

Indbinding Hardback

Sider 552

Udgave

Længde 33mm

Bredde 236mm

Højde 187mm

Udg. Dato 13 nov 2018

Oplagsdato 13 nov 2018

Forlag MIT Press Ltd