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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VIME: Variational Information Maximizing Exploration · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1605.09674/</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/1605.09674/feed.xml" rel="self" type="application/rss+xml" /><item><title>VIME: Variational Information Maximizing Exploration — mentioned by atschantz</title><link>https://news.ycombinator.com/item?id=18495534</link><guid isPermaLink="false">urn:hackerpapers:mention:1605.09674:18495534</guid><pubDate>Tue, 20 Nov 2018 17:20:31 GMT</pubDate><description>&lt;p&gt;There is a large overlap, for instance, the popular VIME exploration algorithm [1] uses part of the free energy objective function. However, free energy isn&amp;#x27;t a theory of curiosity per se, its posed as description of self-organisation. It just so happens that you can express the free energy functional in terms of epistemic (curious) and instrumental (reward) components. [1] https://arxiv.org/abs/1605.09674&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1605.09674/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>VIME: Variational Information Maximizing Exploration — mentioned by andreyk</title><link>https://news.ycombinator.com/item?id=11918766</link><guid isPermaLink="false">urn:hackerpapers:mention:1605.09674:11918766</guid><pubDate>Thu, 16 Jun 2016 20:51:48 GMT</pubDate><description>&lt;p&gt;Brief summary: a nice intro about what generative models are and the current popular approaches/papers, followed by descriptions of recent work by OpenAI in the space. Quick links to papers mentioned: Improving GANs https://arxiv.org/abs/1606.03498 Improving VAEs http://arxiv.org/abs/1606.04934 InfoGAN https://arxiv.org/abs/1606.03657 Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks http://arxiv.org/abs/1605.09674 Generative Adversarial Imitation Learning http://arxiv.org/abs/1606.03476 I think the last one seems very exciting, I expect Imitation…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1605.09674/"&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>
