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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2005.01643/</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/2005.01643/feed.xml" rel="self" type="application/rss+xml" /><item><title>Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems — mentioned by dimatura</title><link>https://news.ycombinator.com/item?id=27882191</link><guid isPermaLink="false">urn:hackerpapers:mention:2005.01643:27882191</guid><pubDate>Mon, 19 Jul 2021 13:04:21 GMT</pubDate><description>&lt;p&gt;Yes. Unfortunately my experience in this area is about four years old, so maybe obsolete at this point. But at that time, for our problem -- which used many of the typical benchmark gym simulators -- the main bottleneck were CPUs, not GPUs. I suspect the equation changes on how complex the neural network is (if it&amp;#x27;s simple, not much is gained from GPU), whether the simulation can take advantage of GPUs (the ones we used didn&amp;#x27;t, but for 3D graphics-heavy simulation and other kinds of computation I&amp;#x27;m sure it can help) and the algorithm -- some algorithms rely more on on-line evaluation and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2005.01643/"&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>
