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回归性能评估指标
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聚类性能评估指标
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使用sklearn进行机器学习
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</a>
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综合实战案例
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5 years ago
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泰坦尼克生还预测
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5 years ago
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5 years ago
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简介
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5 years ago
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探索性数据分析(EDA)
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特征工程
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<a href="../titanic/fit and predict.html">
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构建模型进行预测
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</a>
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<a href="../titanic/tuning.html">
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调参
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</a>
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5 years ago
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<li class="chapter " data-level="1.6.2" >
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5 years ago
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<span>
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使用强化学习玩乒乓球游戏
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter active" data-level="1.6.2.1" data-path="what is reinforce learning.html">
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5 years ago
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<a href="what is reinforce learning.html">
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什么是强化学习
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</a>
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5 years ago
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<li class="chapter " data-level="1.6.2.2" data-path="Policy Gradient.html">
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5 years ago
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<a href="Policy Gradient.html">
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Policy Gradient原理
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</a>
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<li class="chapter " data-level="1.6.2.3" data-path="coding.html">
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5 years ago
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<a href="coding.html">
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使用Policy Gradient玩乒乓球游戏
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</a>
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</ul>
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<li class="chapter " data-level="1.7" data-path="../recommand.html">
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<a href="../recommand.html">
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实训推荐
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<a href=".." >什么是强化学习</a>
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<h1 id="什么是强化学习">什么是强化学习</h1>
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<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>
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<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>
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<p>让我们想象一下比赛现场：</p>
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<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>
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<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>
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<p><img src="../img/1.jpg" alt=""></p>
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<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>
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<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>
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<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>
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<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>
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<p><img src="../img/2.jpg" alt=""></p>
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<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>
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<p><img src="../img/3.jpg" alt=""></p>
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