April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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636
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第十八章：強化學習
print
("
\r
{} loss:{:.5f}".format(
iteration, train_loss.loss.numpy()), end="")
if
iteration % 1000 == 0:
log_metrics(train_metrics)
函式先向
collect policy
取得它的初始狀態
（
提供環境批次大小
，
在此是
1）。
因為策略是
無狀態的
，
它會回傳一個空
tuple（
所以我們也可以寫成
policy_state = ()
）。
接著
，
我
們建立一個處理資料組的
iterator，
並執行訓練迴圈
。
在每一次迭代
，
我們呼叫
driver
的
run()
方法
，
將目前的時步
（
最初是
None
）
和目前的策略狀態傳給它
。
它會執行收集策
略和收集經驗四步
（
就像我們之前設置的
），
將收集的軌跡廣播給重播緩衝區和評量標
準
。
接下來
，
我們從資料組抽出一批軌跡
，
並將它傳給
agent
的
train()
方法
。
它會回傳
一個
train_loss
物件
，
這個物件可能隨著
agent
的類型而不同
。
接下來
，
我們顯示迭代次
數與訓練
loss，
並且每隔
1,000
次迭代就
log
所有評量標準
。
現在你可以直接呼叫
train_
agent()
來執行幾次迭代
，
看看
agent
如何逐漸學習
打磚塊
！
train_agent(10000000)
這將花費許多計算資源
，
也需要你的耐心
（
可能花好幾個小時
，
甚至好幾天
，
依你的硬體
而定
），
而且你可能需要執行演算法多次
，
使用不同的隨機種子
，
才能得到很好的結果
，
但是當你完成時
，age ...
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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.