<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>游戏AI - 标签 - 研发日志 · R&amp;D Log</title><link>https://rd163.visword.com/tags/%E6%B8%B8%E6%88%8Fai/</link><description>游戏AI - 标签 - 研发日志 · R&amp;D Log</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>whutluohui@gmail.com (小智晖)</managingEditor><webMaster>whutluohui@gmail.com (小智晖)</webMaster><copyright>本作品采用知识共享署名-非商业性使用 4.0 国际许可协议进行许可。</copyright><lastBuildDate>Sun, 12 Jan 2025 00:00:00 +0800</lastBuildDate><atom:link href="https://rd163.visword.com/tags/%E6%B8%B8%E6%88%8Fai/" rel="self" type="application/rss+xml"/><item><title>游戏强化训练</title><link>https://rd163.visword.com/posts/game-rl-trainning/</link><pubDate>Sun, 12 Jan 2025 00:00:00 +0800</pubDate><author><name>小智晖</name></author><guid>https://rd163.visword.com/posts/game-rl-trainning/</guid><description>&lt;p>游戏是强化学习(Reinforcement Learning, RL)最经典也最成熟的实验场。规则清晰、状态可观测、奖励可量化、能够无限次对弈——这些特性让棋类、电子竞技、Atari 游戏成为验证 RL 算法的天然沙盒。本文梳理「游戏强化训练」涉及的核心概念、关键里程碑、主流框架,以及代表性开源复现项目。&lt;/p></description></item></channel></rss>