Reinforcement Learning 2025 pdf epub mobi 电子书

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Reinforcement Learning 2025 pdf epub mobi 电子书著者简介

Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.

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发表于2025-01-30

Reinforcement Learning 2025 pdf epub mobi 电子书

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可以在线阅读，还不错的我还没仔细读，先把网址公布出来，大家一起学习 http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

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可以在线阅读，还不错的我还没仔细读，先把网址公布出来，大家一起学习 http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

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[http://incompleteideas.net/book/the-book-2nd.html] 有 [第二版的 PDF(][http://incompleteideas.net/book/bookdraft2018jan1.pdf)][ ]，还有 [Python 实现]([https://github.com/ShangtongZhang/reinforcement-learning-an-introduction])。

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出版者:A Bradford Book

作者:Richard S. Sutton

出品人:

页数:552

译者:

出版时间:2018-11-13

价格:USD 76.00

装帧:精装

isbn号码:9780262039246

丛书系列:

图书标签: 强化学习机器学习人工智能 RL 计算机科学数学 MachineLearning 计算机

Reinforcement Learning 2025 pdf epub mobi 电子书图书描述

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.

Reinforcement Learning 2025 pdf epub mobi 电子书

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