<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning on Arshad Siddiqui</title><link>https://arshadhs.github.io/tags/reinforcement-learning/</link><description>Recent content in Reinforcement Learning on Arshad Siddiqui</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://arshadhs.github.io/tags/reinforcement-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Reinforcement Learning</title><link>https://arshadhs.github.io/docs/ai/machine-learning/ml-reinforcement/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://arshadhs.github.io/docs/ai/machine-learning/ml-reinforcement/</guid><description>&lt;h1 id="reinforcement-learning-rl">
 Reinforcement Learning (RL)
 
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&lt;p>RL is learning by &lt;strong>trial and error&lt;/strong>.&lt;/p>
&lt;p>Reinforcement Learning (RL) is a type of machine learning where an &lt;strong>autonomous agent learns to make decisions by interacting with an environment&lt;/strong>.&lt;/p>
&lt;p>Instead of being told the correct answer, the agent:&lt;/p>
&lt;ul>
&lt;li>takes actions&lt;/li>
&lt;li>observes outcomes&lt;/li>
&lt;li>receives rewards or penalties&lt;/li>
&lt;li>gradually learns a strategy that maximises long-term reward&lt;/li>
&lt;/ul>

&lt;blockquote class='book-hint '>
 &lt;p>&lt;strong>Reinforcement Learning teaches an agent how to act, not what to predict.&lt;/strong>&lt;/p></description></item></channel></rss>