<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Q-Learning on Wang Huijiu's Site</title><link>https://styleofwong.cn/en/tags/q-learning/</link><description>Recent content in Q-Learning on Wang Huijiu's Site</description><image><title>Wang Huijiu's Site</title><url>https://styleofwong.cn/apple-touch-icon.png</url><link>https://styleofwong.cn/apple-touch-icon.png</link></image><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sun, 28 Jun 2026 18:00:00 +0800</lastBuildDate><atom:link href="https://styleofwong.cn/en/tags/q-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Introduction to Reinforcement Learning: From Intuition to Q-Learning</title><link>https://styleofwong.cn/en/posts/reinforcement-learning-intro/</link><pubDate>Sun, 28 Jun 2026 18:00:00 +0800</pubDate><guid>https://styleofwong.cn/en/posts/reinforcement-learning-intro/</guid><description>Reinforcement learning is the paradigm that lets AI learn to &amp;ldquo;grow through trial and error&amp;rdquo;. Starting from the most fundamental intuition, this article clearly explains MDP, the Bellman equation, and Q-Learning, and includes a runnable Python example.</description></item></channel></rss>