April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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622
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第十八章：強化學習
問題的一種做法是使用可以輸出觀察的環境包裝
，
包裝輸出的觀察是以沿著通道維度堆疊
的多幀畫面組成的
。
FrameStack4
包裝正是實作這種策略
，
它會回傳包含四幀的堆疊
。
我
們來建立包裝起來的
Atari
環境
！
from tf_agents.environments import
suite_atari
from tf_agents.environments.atari_preprocessing import
AtariPreprocessing
from tf_agents.environments.atari_wrappers import
FrameStack4
max_episode_steps = 27000 # <=> 108k ALE
幀
，
因為
1
步
= 4
幀
environment_name = "BreakoutNoFrameskip-v4"
env = suite_atari.load(
environment_name,
max_episode_steps=max_episode_steps,
gym_env_wrappers=[AtariPreprocessing, FrameStack4])
圖
18-12
是上面的前置作業的結果
。
你可以看到解析度低很多
，
但仍然足以玩這個遊戲
。
此外
，
畫格是沿著通道維度堆疊的
，
所以紅色代表三步之前的畫格
，
綠色代表兩步之前
，
藍色是上一個畫格
，
粉紅色是目前的畫格
20
。
從這一個觀察
，agent
可以看到球朝著左下角
前進
，
所以它應該將桿子繼續移到左邊 ...
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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.