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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Minimal Active Inference Agent · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1503.04187/</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/1503.04187/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Minimal Active Inference Agent — mentioned by eli_gottlieb</title><link>https://news.ycombinator.com/item?id=12320613</link><guid isPermaLink="false">urn:hackerpapers:mention:1503.04187:12320613</guid><pubDate>Fri, 19 Aug 2016 15:18:28 GMT</pubDate><description>&lt;p&gt;&amp;gt;&amp;quot;Hard&amp;quot; problems are multi-optimization, where even the definitions aren&amp;#x27;t very clear: &amp;quot;learn as much as possible and live a happy life while making a productive living for yourself&amp;quot;. Turn that into an objective function... Minimize the informational free-energy of the product of the multiplicatively inverted brain-to-body reinforcement-learned energy function of reward with the brain-to-body reinforcement-learned energy function of punishment, via active inference? Beh, those words are messy. The point is to talk about a Gibbs distribution with energy functions Reward(X, Y) and Punishment(X…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1503.04187/"&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>
