You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
610 lines
22 KiB
610 lines
22 KiB
|
|
<!DOCTYPE HTML>
|
|
<html lang="" >
|
|
<head>
|
|
<meta charset="UTF-8">
|
|
<meta content="text/html; charset=utf-8" http-equiv="Content-Type">
|
|
<title>什么是强化学习 · GitBook</title>
|
|
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
|
|
<meta name="description" content="">
|
|
<meta name="generator" content="GitBook 3.2.3">
|
|
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="../gitbook/style.css">
|
|
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-katex/katex.min.css">
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-highlight/website.css">
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-search/search.css">
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="../gitbook/gitbook-plugin-fontsettings/website.css">
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<meta name="HandheldFriendly" content="true"/>
|
|
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no">
|
|
<meta name="apple-mobile-web-app-capable" content="yes">
|
|
<meta name="apple-mobile-web-app-status-bar-style" content="black">
|
|
<link rel="apple-touch-icon-precomposed" sizes="152x152" href="../gitbook/images/apple-touch-icon-precomposed-152.png">
|
|
<link rel="shortcut icon" href="../gitbook/images/favicon.ico" type="image/x-icon">
|
|
|
|
|
|
<link rel="next" href="Policy Gradient.html" />
|
|
|
|
|
|
|
|
</head>
|
|
<body>
|
|
|
|
<div class="book">
|
|
<div class="book-summary">
|
|
|
|
|
|
<div id="book-search-input" role="search">
|
|
<input type="text" placeholder="Type to search" />
|
|
</div>
|
|
|
|
|
|
<nav role="navigation">
|
|
|
|
|
|
|
|
<ul class="summary">
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<li class="chapter " data-level="1.1" data-path="../">
|
|
|
|
<a href="../">
|
|
|
|
|
|
简介
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.2" data-path="../machine_learning.html">
|
|
|
|
<a href="../machine_learning.html">
|
|
|
|
|
|
机器学习概述
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3" data-path="../algorithm.html">
|
|
|
|
<a href="../algorithm.html">
|
|
|
|
|
|
常见机器学习算法
|
|
|
|
</a>
|
|
|
|
|
|
|
|
<ul class="articles">
|
|
|
|
|
|
<li class="chapter " data-level="1.3.1" data-path="../kNN.html">
|
|
|
|
<a href="../kNN.html">
|
|
|
|
|
|
近朱者赤近墨者黑-kNN
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.2" data-path="../linear_regression.html">
|
|
|
|
<a href="../linear_regression.html">
|
|
|
|
|
|
最简单的回归算法-线性回归
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.3" data-path="../logistic_regression.html">
|
|
|
|
<a href="../logistic_regression.html">
|
|
|
|
|
|
使用回归的思想进行分类-逻辑回归
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.4" data-path="../decision_tree.html">
|
|
|
|
<a href="../decision_tree.html">
|
|
|
|
|
|
最接近人类思维的算法-决策树
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.5" data-path="../random_forest.html">
|
|
|
|
<a href="../random_forest.html">
|
|
|
|
|
|
群众的力量是伟大的-随机森林
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.6" data-path="../kMeans.html">
|
|
|
|
<a href="../kMeans.html">
|
|
|
|
|
|
物以类聚人以群分-kMeans
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.3.7" data-path="../AGNES.html">
|
|
|
|
<a href="../AGNES.html">
|
|
|
|
|
|
以距离为尺-AGNES
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
|
|
</ul>
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.4" data-path="../metrics.html">
|
|
|
|
<a href="../metrics.html">
|
|
|
|
|
|
模型评估指标
|
|
|
|
</a>
|
|
|
|
|
|
|
|
<ul class="articles">
|
|
|
|
|
|
<li class="chapter " data-level="1.4.1" data-path="../classification_metrics.html">
|
|
|
|
<a href="../classification_metrics.html">
|
|
|
|
|
|
分类性能评估指标
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.4.2" data-path="../regression_metrics.html">
|
|
|
|
<a href="../regression_metrics.html">
|
|
|
|
|
|
回归性能评估指标
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.4.3" data-path="../cluster_metrics.html">
|
|
|
|
<a href="../cluster_metrics.html">
|
|
|
|
|
|
聚类性能评估指标
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
|
|
</ul>
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.5" data-path="../sklearn.html">
|
|
|
|
<a href="../sklearn.html">
|
|
|
|
|
|
使用sklearn进行机器学习
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6" >
|
|
|
|
<span>
|
|
|
|
|
|
综合实战案例
|
|
|
|
</span>
|
|
|
|
|
|
|
|
<ul class="articles">
|
|
|
|
|
|
<li class="chapter " data-level="1.6.1" >
|
|
|
|
<span>
|
|
|
|
|
|
泰坦尼克生还预测
|
|
|
|
</span>
|
|
|
|
|
|
|
|
<ul class="articles">
|
|
|
|
|
|
<li class="chapter " data-level="1.6.1.1" data-path="../titanic/introduction.html">
|
|
|
|
<a href="../titanic/introduction.html">
|
|
|
|
|
|
简介
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.1.2" data-path="../titanic/EDA.html">
|
|
|
|
<a href="../titanic/EDA.html">
|
|
|
|
|
|
探索性数据分析(EDA)
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.1.3" data-path="../titanic/feature engerning.html">
|
|
|
|
<a href="../titanic/feature engerning.html">
|
|
|
|
|
|
特征工程
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.1.4" data-path="../titanic/fit and predict.html">
|
|
|
|
<a href="../titanic/fit and predict.html">
|
|
|
|
|
|
构建模型进行预测
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.1.5" data-path="../titanic/tuning.html">
|
|
|
|
<a href="../titanic/tuning.html">
|
|
|
|
|
|
调参
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
|
|
</ul>
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.2" >
|
|
|
|
<span>
|
|
|
|
|
|
使用强化学习玩乒乓球游戏
|
|
|
|
</span>
|
|
|
|
|
|
|
|
<ul class="articles">
|
|
|
|
|
|
<li class="chapter active" data-level="1.6.2.1" data-path="what is reinforce learning.html">
|
|
|
|
<a href="what is reinforce learning.html">
|
|
|
|
|
|
什么是强化学习
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.2.2" data-path="Policy Gradient.html">
|
|
|
|
<a href="Policy Gradient.html">
|
|
|
|
|
|
Policy Gradient原理
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.6.2.3" data-path="coding.html">
|
|
|
|
<a href="coding.html">
|
|
|
|
|
|
使用Policy Gradient玩乒乓球游戏
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
|
|
</ul>
|
|
|
|
</li>
|
|
|
|
|
|
</ul>
|
|
|
|
</li>
|
|
|
|
<li class="chapter " data-level="1.7" data-path="../recommand.html">
|
|
|
|
<a href="../recommand.html">
|
|
|
|
|
|
实训推荐
|
|
|
|
</a>
|
|
|
|
|
|
|
|
</li>
|
|
|
|
|
|
|
|
|
|
<li class="divider"></li>
|
|
|
|
<li>
|
|
<a href="https://www.gitbook.com" target="blank" class="gitbook-link">
|
|
Published with GitBook
|
|
</a>
|
|
</li>
|
|
</ul>
|
|
|
|
|
|
</nav>
|
|
|
|
|
|
</div>
|
|
|
|
<div class="book-body">
|
|
|
|
<div class="body-inner">
|
|
|
|
|
|
|
|
<div class="book-header" role="navigation">
|
|
|
|
|
|
<!-- Title -->
|
|
<h1>
|
|
<i class="fa fa-circle-o-notch fa-spin"></i>
|
|
<a href=".." >什么是强化学习</a>
|
|
</h1>
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<div class="page-wrapper" tabindex="-1" role="main">
|
|
<div class="page-inner">
|
|
|
|
<div id="book-search-results">
|
|
<div class="search-noresults">
|
|
|
|
<section class="normal markdown-section">
|
|
|
|
<h1 id="什么是强化学习">什么是强化学习</h1>
|
|
<p>强化学习是一类算法，是让计算机实现从一开始完全随机的进行操作，通过不断地尝试，从错误中学习，最后找到规律，学会了达到目的的方法。这就是一个完整的强化学习过程。让计算机在不断的尝试中更新自己的行为，从而一步步学习如何操自己的行为得到高分。</p>
|
|
<p>它主要包含四个元素，Agent、环境状态、行动、奖励，强化学习的目标就是获得最多的累计奖励。</p>
|
|
<p>让我们想象一下比赛现场：</p>
|
|
<p>计算机有一位虚拟的裁判，这个裁判他不会告诉你如何行动，如何做决定，他为你做的事只有给你的行为打分，最开始，计算机完全不知道该怎么做，行为完全是随机的，那计算机应该以什么形式学习这些现有的资源，或者说怎么样只从分数中学习到我应该怎样做决定呢？很简单，只需要记住那些高分，低分对应的行为，下次用同样的行为拿高分, 并避免低分的行为。</p>
|
|
<p>计算机就是 Agent，他试图通过采取行动来操纵环境，并且从一个状态转变到另一个状态，当他完成任务时给高分(奖励)，但是当他没完成任务时，给低分(无奖励)。这也是强化学习的核心思想。</p>
|
|
<p><img src="../img/1.jpg" alt=""></p>
|
|
<p>在强化学习中有很多算法，如果按类别划分可以划分成 model-based (基于模型)和 model-free (不基于模型)两大类。</p>
|
|
<p>如果我们的 Agent 不理解环境，环境给了什么就是什么，我们就把这种方法叫做 model-free，这里的 model 就是用模型来表示环境，理解环境就是学会了用一个模型来代表环境，所以这种就是 model-based 方法。</p>
|
|
<p>Model-free 的方法有很多, 像 Q learning、Sarsa、Policy Gradients 都是从环境中得到反馈然后从中学习。而 model-based 只是多了一道程序，为真实世界建模，也可以说他们都是 model-free 的强化学习, 只是 Model-based 多出了一个虚拟环境，我们可以先在虚拟环境中尝试，如果没问题，再拿到现实环境中来。</p>
|
|
<p>model-free 中, Agent 只能按部就班，一步一步等待真实世界的反馈，再根据反馈采取下一步行动。而 model-based，能通过想象来预判断接下来将要发生的所有情况，然后选择这些想象情况中最好的那种，并依据这种情况来采取下一步的策略，这也就是围棋场上 AlphaGo 能够超越人类的原因。</p>
|
|
<p><img src="../img/2.jpg" alt=""></p>
|
|
<p>在这里主要介绍一下 model-free 中基于策略的一种算法，Policy Gradient。在介绍该算法之前，我们先要明确一下这个雅达利乒乓球游戏中的<strong>环境状态</strong>是游戏画面，<strong>Agent</strong>是我们操作的挡板，<strong>奖励</strong>是分数，<strong>动作</strong>是上或者下。</p>
|
|
<p><img src="../img/3.jpg" alt=""></p>
|
|
|
|
|
|
</section>
|
|
|
|
</div>
|
|
<div class="search-results">
|
|
<div class="has-results">
|
|
|
|
<h1 class="search-results-title"><span class='search-results-count'></span> results matching "<span class='search-query'></span>"</h1>
|
|
<ul class="search-results-list"></ul>
|
|
|
|
</div>
|
|
<div class="no-results">
|
|
|
|
<h1 class="search-results-title">No results matching "<span class='search-query'></span>"</h1>
|
|
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
</div>
|
|
</div>
|
|
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<a href="Policy Gradient.html" class="navigation navigation-next navigation-unique" aria-label="Next page: Policy Gradient原理">
|
|
<i class="fa fa-angle-right"></i>
|
|
</a>
|
|
|
|
|
|
|
|
</div>
|
|
|
|
<script>
|
|
var gitbook = gitbook || [];
|
|
gitbook.push(function() {
|
|
gitbook.page.hasChanged({"page":{"title":"什么是强化学习","level":"1.6.2.1","depth":3,"next":{"title":"Policy Gradient原理","level":"1.6.2.2","depth":3,"path":"pingpong/Policy Gradient.md","ref":"./pingpong/Policy Gradient.md","articles":[]},"previous":{"title":"使用强化学习玩乒乓球游戏","level":"1.6.2","depth":2,"ref":"","articles":[{"title":"什么是强化学习","level":"1.6.2.1","depth":3,"path":"pingpong/what is reinforce learning.md","ref":"./pingpong/what is reinforce learning.md","articles":[]},{"title":"Policy Gradient原理","level":"1.6.2.2","depth":3,"path":"pingpong/Policy Gradient.md","ref":"./pingpong/Policy Gradient.md","articles":[]},{"title":"使用Policy Gradient玩乒乓球游戏","level":"1.6.2.3","depth":3,"path":"pingpong/coding.md","ref":"./pingpong/coding.md","articles":[]}]},"dir":"ltr"},"config":{"gitbook":"*","theme":"default","variables":{},"plugins":["katex"],"pluginsConfig":{"katex":{},"highlight":{},"search":{},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"fontsettings":{"theme":"white","family":"sans","size":2},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"showLevel":false}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"}},"file":{"path":"pingpong/what is reinforce learning.md","mtime":"2019-07-05T01:27:11.061Z","type":"markdown"},"gitbook":{"version":"3.2.3","time":"2019-07-06T07:31:21.537Z"},"basePath":"..","book":{"language":""}});
|
|
});
|
|
</script>
|
|
</div>
|
|
|
|
|
|
<script src="../gitbook/gitbook.js"></script>
|
|
<script src="../gitbook/theme.js"></script>
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-search/search-engine.js"></script>
|
|
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-search/search.js"></script>
|
|
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-lunr/lunr.min.js"></script>
|
|
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-lunr/search-lunr.js"></script>
|
|
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-sharing/buttons.js"></script>
|
|
|
|
|
|
|
|
<script src="../gitbook/gitbook-plugin-fontsettings/fontsettings.js"></script>
|
|
|
|
|
|
|
|
</body>
|
|
</html>
|
|
|