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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2208.07860/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2208.07860/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning — mentioned by mitchellgoffpc</title><link>https://news.ycombinator.com/item?id=32775026</link><guid isPermaLink="false">urn:hackerpapers:mention:2208.07860:32775026</guid><pubDate>Fri, 09 Sep 2022 03:36:45 GMT</pubDate><description>&lt;p&gt;Some neat results from the last six months or so: - Significantly-improved diffusion models (DALL-E 2, Midjourney, Stable Diffusion, etc) - Diffusion models for video (see https://video-diffusion.github.io/, this paper is from April but I expect to see a lot more published research in this area soon) - OpenAI Minecraft w/VPT (first model with non-zero success rate at mining diamonds in &amp;lt;20min) - AlphaCode (from February, reasonably high success rate on solving competitive programming problems) - Improved realism and scale for NeRFs (see https://dellaert.github.io/NeRF22/ for some cool…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2208.07860/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
