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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1511.06342/</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/1511.06342/feed.xml" rel="self" type="application/rss+xml" /><item><title>Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning — mentioned by gwern</title><link>https://news.ycombinator.com/item?id=11865011</link><guid isPermaLink="false">urn:hackerpapers:mention:1511.06342:11865011</guid><pubDate>Wed, 08 Jun 2016 19:41:51 GMT</pubDate><description>&lt;p&gt;You would get some boost if you use a NN factored to have game-specific module feeding into a universal game-playing NN: https://arxiv.org/abs/1511.06342 &amp;quot; Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning&amp;quot;, Parisotto et al 2015 &amp;gt; The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1511.06342/"&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>
