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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>JaxMARL: Multi-Agent RL Environments and Algorithms in JAX · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2311.10090/</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/2311.10090/feed.xml" rel="self" type="application/rss+xml" /><item><title>JaxMARL: Multi-Agent RL Environments and Algorithms in JAX — mentioned by zengid</title><link>https://news.ycombinator.com/item?id=43106202</link><guid isPermaLink="false">urn:hackerpapers:mention:2311.10090:43106202</guid><pubDate>Wed, 19 Feb 2025 19:19:16 GMT</pubDate><description>&lt;p&gt;There is some research in accelerating Reinforcement Learning by implementing the simulator on the GPU using Jax. Really neat. I&amp;#x27;m curious if this could be done with Mojo, too? https://arxiv.org/html/2311.10090v5 https://youtu.be/Jr_nGkCG3og?si=WKS7Hbz13A0QH4nf&amp;amp;t=520&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2311.10090/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
