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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Time Limits in Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.00378/</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/1712.00378/feed.xml" rel="self" type="application/rss+xml" /><item><title>Time Limits in Reinforcement Learning — mentioned by jimfleming</title><link>https://news.ycombinator.com/item?id=19147030</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.00378:19147030</guid><pubDate>Tue, 12 Feb 2019 20:09:40 GMT</pubDate><description>&lt;p&gt;Hopefully this leads to better temporal priors for machine learning models. Sequence encoding frequently uses position coding[0] but the typical approaches (e.g. low frequency sin waves) are fairly naive. In reinforcement learning we have (somewhat obvious) works that show that providing explicit time representations help[1]. Simply throwing a recurrent model at a sequence may pick up simple ordering but improving the temporal representation would improve data efficiency. [0] https://arxiv.org/abs/1706.03762 [1] https://arxiv.org/abs/1712.00378&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.00378/"&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>
