<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Experience Replay on Chen Kai Blog</title><link>https://www.chenk.top/en/tags/experience-replay/</link><description>Recent content in Experience Replay on Chen Kai Blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Wed, 06 Aug 2025 09:00:00 +0000</lastBuildDate><atom:link href="https://www.chenk.top/en/tags/experience-replay/index.xml" rel="self" type="application/rss+xml"/><item><title>Reinforcement Learning (2): Q-Learning and Deep Q-Networks (DQN)</title><link>https://www.chenk.top/en/reinforcement-learning/02-q-learning-and-dqn/</link><pubDate>Wed, 06 Aug 2025 09:00:00 +0000</pubDate><guid>https://www.chenk.top/en/reinforcement-learning/02-q-learning-and-dqn/</guid><description>&lt;p>In December 2013, a small DeepMind team uploaded a paper to arXiv with a striking claim: a single neural network, trained from raw pixels and the score, learned to play seven Atari games — and beat the previous best on six of them. No game-specific features. No hand-coded heuristics. The same architecture for Pong, Breakout, and Space Invaders. The algorithm was &lt;strong>Deep Q-Network (DQN)&lt;/strong>, and it kicked off the deep reinforcement learning era.&lt;/p></description></item></channel></rss>