<?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>强化学习 on Blog</title><link>https://icelemon.top/tags/%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/</link><description>Recent content in 强化学习 on Blog</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Sat, 24 Oct 2026 00:00:00 +0800</lastBuildDate><atom:link href="https://icelemon.top/tags/%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0/index.xml" rel="self" type="application/rss+xml"/><item><title>具身学习项目：经典策略、BC与REINFORCE对照</title><link>https://icelemon.top/tech/robotics/embodied-learning-project/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/embodied-learning-project/</guid><description>在可复现的一维闭环中完成合成演示、BC与RL训练、独立评估和梯度检查，并提供PyTorch对照。</description></item><item><title>强化学习：MDP、策略梯度与机器人约束</title><link>https://icelemon.top/tech/robotics/reinforcement-learning/</link><pubDate>Sat, 24 Oct 2026 00:00:00 +0800</pubDate><guid>https://icelemon.top/tech/robotics/reinforcement-learning/</guid><description>明确状态、动作、奖励和终止，用可运行REINFORCE实验理解回报、基线与闭环评估。</description></item></channel></rss>