April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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585
第十八章
強化學習
強化學習
（Reinforcement Learning，RL）
是當今最振奮人心的機器學習領域
，
也是最古老
的一種
。
它早在
1950
年代就出現了
，
多年來產生許多有趣的應用
1
，
尤其是在遊戲中
（
例
如
TD-Gammon，
這是一種西洋雙陸棋
（Backgammon）
遊戲
）
以及機器控制
，
但很少成為
頭條新聞
。
但是
2013
年發生了一場革命
，
當時的英國初創公司
DeepMind
研究人員展示
了一種系統
，
這種系統可以從頭學習幾乎所有
Atari
遊戲
（(https://homl.info/dqn）
2
，
最終
在大部分遊戲中取得遠優於人類的表現
（https://homl.info/dqn2）
3
，
它的輸入只有原始的
像素
，
而且不需要事先瞭解遊戲規則
4
。
這只是他們一系列壯舉的開端而已
，
他們在
2016
年
3
月到達顛峰
，
用
AlphaGo
系統打敗圍棋傳奇職業棋手李世
乭
，
並在
2017
年
5
月打敗
世界冠軍柯傑
，
在這之前沒有任何電腦程式能夠與圍棋的大師一較高下
，
更不用說勝過世
界冠軍了
。
如今
，RL
的領域充滿了新點子
，
並且有廣泛的應用
。Google
在
2014
年以超過
5
億美元的價格收購
DeepMind。
那麼
，DeepMind
究竟是如何取得這些成就的
？
事後看來
，
他們的做法很簡單
：
他們將深
度學習的威力應用在強化學習領域
，
取得遠超過他們最瘋狂的夢想的成果
。
本章將先解釋
什麼是強化學習
，
以及它的好處是什麼
，
接著介紹兩種最重要的深度強化學習技術
：
策略
梯度
（policy gradient）
以及
深層
Q
網
（deep Q-networks ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.