January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
Content preview from 深度學習｜內行人的做法
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451
附錄 B
RL4J 與強化學習
Ruben Fiszel
（
http://rubenfiszel.github.io/
）
序言
本附錄一開始先介紹「強化學習（reinforcement learning）」，然後再詳細說明以像素為
輸入的深度 Q 網路（DQN, Deep Q-Networks），最後展示一個 RL4J 範例來做個小結。
我們先來看看強化學習的核心概念。
強化學習是機器學習領域中一個令人興奮的子領域。基本上，它指的是在特定環境中
學習高效的策略。如果用比較非正式的說法，它其實與「帕夫洛夫制約」（Pavlovian
conditioning，又稱「古典制約」）非常相似：如果你針對某個行為給予獎勵，經過一段
時間之後，代理者就能學會重複這樣的行為，以獲得更多的獎勵。
馬可夫決策過程
從形式上來看，前面所提到的環境，可以用所謂的「馬可夫決策過程（MDP, Markov
Decision Process）」來加以定義。這個聽起來很可怕的名字，其實就是以下（五元組）
所構成的組合：
• 一組狀態（State）S：例如在西洋棋中，狀態指的就是各種棋局下每個棋子的位置。
• 一組可能的動作（Action）A：在西洋棋中，指的就是每一種棋局下每一種可能的動
作（例如從 e4 移動到 e5）。
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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.