Reinforcement Learning pdf epub mobi txt 電子書 下載2025
- 機器學習
- 強化學習
- 人工智能
- AI
- Reinforcement
- 計算機科學
- 增強學習
- 計算機
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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
具體描述
讀後感
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)。
評分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)。
評分这是一本极好的书，不仅能使你对强化学习有精确、透彻的理解，更能够提升你的思维层次。 接触人工智能领域6年多了，用过统计学习和深度学习做过一些项目。目前，David Silver的教学视频已经过完，这本书读到了第10章（第二版）。下面说一下个人浅陋的理解。 目前应用最广泛的监...
評分可以在线阅读，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
評分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)。
用戶評價
經過一個多月的奮戰終於看完瞭，感覺講得太天馬行空瞭。就算已經有瞭點基礎知識瞭，看起來還是很難 follow。
评分future
评分介紹性較強,實用性不夠,是把整個RL曆史和所有的算法都介紹瞭一遍,但實際上Q-learning已經占據統治地位,前麵的兩章算是鋪墊. 要看實際的例子和代碼還是去看 AI- modern approach.
评分作為一個學渣 看一會就打瞌睡???? 硬著頭皮看瞭一半，終於還是放棄瞭T-T
评分入門必讀，可結閤david silver 的視頻課程看
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