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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Reinforcement Learning with a Natural Language Action Space · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.04636/</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/1511.04636/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Reinforcement Learning with a Natural Language Action Space — mentioned by hedgehog</title><link>https://news.ycombinator.com/item?id=36489783</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.04636:36489783</guid><pubDate>Tue, 27 Jun 2023 07:07:55 GMT</pubDate><description>&lt;p&gt;There is a fair amount of existing work related to planning in continuous and unbounded spaces under uncertainty. It seems likely some of the existing techniques combined with modern language models could be quite effective. A couple entry points: Deep Reinforcement Learning with an Unbounded Action Space - https://arxiv.org/abs/1511.04636v3 Efficient Planning in a Compact Latent Action Space - https://arxiv.org/abs/2208.10291&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.04636/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
