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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hindsight Experience Replay · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1707.01495/</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/1707.01495/feed.xml" rel="self" type="application/rss+xml" /><item><title>Hindsight Experience Replay — mentioned by johnmoberg</title><link>https://news.ycombinator.com/item?id=22949227</link><guid isPermaLink="false">urn:hackerpapers:mention:1707.01495:22949227</guid><pubDate>Wed, 22 Apr 2020 19:25:20 GMT</pubDate><description>&lt;p&gt;Your last paragraph reminds me of Hindsight Experience Replay[1] where they use goal-conditioned policies that learn how to reach essentially any state. The idea is that, even if you don&amp;#x27;t reach your true goal, you&amp;#x27;ve learned how to reach some other goal, so you can successively learn to navigate the environment until the end goal is easy. It&amp;#x27;s a really nice paper, highly recommended! [1] https://arxiv.org/abs/1707.01495&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1707.01495/"&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>
