July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Content preview from 深度学习入门 : 基于Python的理论与实现
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第 8章 深度学习
262
图8-25 基于深度学习的图像分割的例子：道路、车辆、建筑物、人行道等被高精度地识
别了出来（引用自文献[43]）
8.5.4
Deep Q-Network（强化学习）
就像人类通过摸索试验来学习一样（比如骑自行车），让计算机也在摸索
试验的过程中自主学习，这称为强化学习（
reinforcement learning
）。强化学
习和有“教师”在身边教的“监督学习”有所不同。
强化学习的基本框架是，代理（
Agent
）根据环境选择行动，然后通过这
个行动改变环境。根据环境的变化，代理获得某种报酬。强化学习的目的是
决定代理的行动方针，以获得更好的报酬（图 8
-
26）。
图8
-
26 中展示了强化学习的基本框架。这里需要注意的是，报酬并不是
确定的，只是“预期报酬”。比如，在《超级马里奥兄弟》这款电子游戏中，
让马里奥向右移动能获得多少报酬不一定是明确的。这时需要从游戏得分（获
得的硬币、消灭的敌人等）或者游戏结束等明确的指标来反向计算，决定“预
期报酬”。如果是监督学习的话，每个行动都可以从“教师”那里获得正确的
评价。
在使用了深度学习的强化学习方法中，有一个叫作
Deep Q-Network
（通
称
DQN
）
[44]
的方法。该方法基于被称为
Q
学习的强化学习算法。这里省略
8.5 深度学习的未来 ...
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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.
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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.