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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Q-learning with online random forests · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2204.03771/</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/2204.03771/feed.xml" rel="self" type="application/rss+xml" /><item><title>Q-learning with online random forests — mentioned by markisus</title><link>https://news.ycombinator.com/item?id=38835574</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.03771:38835574</guid><pubDate>Mon, 01 Jan 2024 21:39:20 GMT</pubDate><description>&lt;p&gt;This is a relatively recent paper you might find interesting. https://arxiv.org/abs/2204.03771 A random forest is also a universal function approximator so anything a neural net can do, so can a random forest (in theory). In practice, neural nets are easier for modern hardware to optimize while I think trees incur computational overhead due to branchiness.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.03771/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
