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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1703.05407/</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/1703.05407/feed.xml" rel="self" type="application/rss+xml" /><item><title>Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play — mentioned by richard___</title><link>https://news.ycombinator.com/item?id=16045138</link><guid isPermaLink="false">urn:hackerpapers:mention:1703.05407:16045138</guid><pubDate>Mon, 01 Jan 2018 07:33:42 GMT</pubDate><description>&lt;p&gt;Let&amp;#x27;s say you&amp;#x27;re reading this research paper: https://arxiv.org/pdf/1703.05407.pdf I want to know: &amp;quot;In Rob Fergus&amp;#x27; paper intrinsic motivation and self-play, how is it that the reward for agent A can be based on the time taken for agent B&amp;#x27;s task, when agent A and B are run sequentially, and agent B runs after agent A?&amp;quot; Can Google answer this? No. Is NLP smart enough to answer this? Probably not. But the question itself is hard to write in one sentence. Complex queries are best structured as conversations, with clarifying back and forth, Socratic method style, like if you&amp;#x27;re asking a professor…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1703.05407/"&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>
