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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Conservative Agency via Attainable Utility Preservation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1902.09725/</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/1902.09725/feed.xml" rel="self" type="application/rss+xml" /><item><title>Conservative Agency via Attainable Utility Preservation — mentioned by vonnik</title><link>https://news.ycombinator.com/item?id=21549880</link><guid isPermaLink="false">urn:hackerpapers:mention:1902.09725:21549880</guid><pubDate>Fri, 15 Nov 2019 23:50:29 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s a lot of interesting research focused on this problem space, training algorithms toward a more flexible problem-solving ability. Here are a couple: https://arxiv.org/pdf/1902.09725.pdf https://openreview.net/pdf?id=Skc-Fo4Yg I do think the DeepMind team is pretty aware of how well their algorithms adapt to new problems. That has been a theme since the Atari paper.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1902.09725/"&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>
