Reinforcement Learning pdf epub mobi txt 电子书 下载 2025
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.
- 强化学习
- 机器学习
- 人工智能
- RL
- 计算机科学
- 数学
- MachineLearning
- 计算机
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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读后感
[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])。
评分[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])。
评分可以在线阅读，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
评分[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])。
评分可以在线阅读，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
用户评价
正确的元策略应该有一定的随机性
评分看了前两部分
评分第二版是98年第一版基础上修订的，在原有基础上增加了一些神经网络相关算法。但原有知识架构还是比较老的，不过非常适合刚接触RL的新手。我只看了前三章基础，后面选择性看了几个重要的章节。前三章虽然基础，但内容非常引人入胜，语言逻辑清晰便于理解，从最基础的MDP讲起到Monte Carlo方法、到经典dynamic programing，后面章节再逐渐到用的最广的Q-learning和policy gradient等方法，一步一步加深完善，便于系统学习知识。Richard Sutton的RL经典，推荐！
评分主要是培养一些intuition 可以配合david silver的课程 https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
评分终于全部读完，非常好！堪称强化学习“圣经”。这周末出去，下周回来再做详细书评
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