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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Generative Adversarial Imitation Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1606.03476/</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/1606.03476/feed.xml" rel="self" type="application/rss+xml" /><item><title>Generative Adversarial Imitation Learning — mentioned by andreyk</title><link>https://news.ycombinator.com/item?id=11918766</link><guid isPermaLink="false">urn:hackerpapers:mention:1606.03476:11918766</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/1606.03476/"&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>
