April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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實作 Deep Q-Learning
|
609
公式
18-7
目標
Q
值
有了這個目標
Q
值之後
，
我們就可以用任何一種梯度下降演算法來執行訓練步驟了
。
我
們通常會試著盡量減少估計
Q
值
Q(s, a)
與目標
Q
值之間的誤差
（
或
Huber loss，
以降低
演算法對大型誤差的敏感度
）
的平方
。
以上就是基本
Deep Q-Learning
演算法
！
接下來我
們要瞭解如何實作它
，
來處理
CartPole
環境
。
實作
Deep Q-Learning
首先
，
我們需要一個
Deep Q-Network。
理論上
，
你需要一個接收一對狀態
/
行動
，
並輸出
一個近似
Q
值的神經網路
，
但是在實務上更有效率的做法是使用神經網路
，
讓它接收一
個狀態
，
並為各個可能的行動輸出一個近似
Q
值
。
處理
CartPole
環境不需要很複雜的神
經網路
，
你只要用幾個隱藏層就夠了
：
env = gym.make("CartPole-v0")
input_shape = [4] # == env.observation_space.shape
n_outputs = 2 # == env.action_space.n
model = keras.models.Sequential([
keras.layers.Dense(32, activation="elu", input_shape=input_shape),
keras.layers.Dense(32, activation="elu"),
keras.layers.Dense(n_outputs)
])
用這個
DQN
選擇行動的做法是選出有最大預測 ...
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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.