<?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>CS285 on dextermayhewjd</title><link>https://dextermayhewjd.github.io/tags/cs285/</link><description>Recent content in CS285 on dextermayhewjd</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Wed, 07 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dextermayhewjd.github.io/tags/cs285/index.xml" rel="self" type="application/rss+xml"/><item><title>Lecture 2 · 从行为克隆到 DAgger</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-02/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-02/</guid><description>用监督学习拟合专家动作之后，为什么自己跑起来还会出错？先看整讲主线，再逐行拆解 20 条公式。</description></item><item><title>Lecture 5 · 从期望回报到策略梯度</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-05/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-05/</guid><description>先逐步推导 REINFORCE，再解释 baseline、reward-to-go 与伪损失。每个知识节点旁都有公式，点击可进入详细拆解。</description></item><item><title>Lecture 6 · 从回报估计到 Actor-Critic 与 GAE</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-06/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-06/</guid><description>保留 21 条公式拆解，先看完整循环，再拆 Q/V/A、价值回归、自举、n-step、GAE、replay 与重参数化。算法集中成框，数学依据逐步展开。</description></item><item><title>Lecture 9 · 从旧样本复用到 PPO</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-09/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-09/</guid><description>逐步拆开换分布、轨迹重要性权重、因果性、单步替代目标与 clip/min，保留公式逐行拆解和四篇卡点精读。</description></item><item><title>Lecture 10 · 从策略改进界到 PPO-KL 与 TRPO</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-10/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-10/</guid><description>解释旧优势与旧状态分布的依据，逐步走过望远镜求和、耦合、TV、Pinsker、KL 惩罚和自然梯度。完整循环集中成框，保留 20 条公式拆解。</description></item><item><title>Lecture 14 · 从奖励学习到语言模型强化学习</title><link>https://dextermayhewjd.github.io/courses/cs285/lecture-14/</link><pubDate>Wed, 07 Oct 2026 00:00:00 +0000</pubDate><guid>https://dextermayhewjd.github.io/courses/cs285/lecture-14/</guid><description>保留 22 条公式拆解，先展示完整训练循环，再连接 IRL、对抗学习、LLM 策略、GAE/GRPO、参考 KL、偏好与历史状态。</description></item></channel></rss>