<?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>Policy-Gradient on Xu'Blog</title><link>https://xuquant.com/tags/policy-gradient/</link><description>Recent content in Policy-Gradient on Xu'Blog</description><image><title>Xu'Blog</title><url>https://xuquant.com/images/profile.jpg</url><link>https://xuquant.com/images/profile.jpg</link></image><generator>Hugo -- 0.152.2</generator><language>en</language><lastBuildDate>Wed, 30 Apr 2025 10:00:00 +0800</lastBuildDate><atom:link href="https://xuquant.com/tags/policy-gradient/index.xml" rel="self" type="application/rss+xml"/><item><title>Policy Optimization for End-to-End Autonomous Driving: From REINFORCE to GRPO</title><link>https://xuquant.com/posts/autodrive/rl-policy-optimization-e2e-driving/</link><pubDate>Wed, 30 Apr 2025 10:00:00 +0800</pubDate><guid>https://xuquant.com/posts/autodrive/rl-policy-optimization-e2e-driving/</guid><description>A systematic derivation of policy optimization methods for end-to-end autonomous driving: from REINFORCE through PPO to GRPO, covering advantage estimation, sampling differences between LLM and driving, multi-objective loss design, and the role of noise in diffusion-based exploration.</description></item></channel></rss>