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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.00625/</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/1611.00625/feed.xml" rel="self" type="application/rss+xml" /><item><title>TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games — mentioned by philips</title><link>https://news.ycombinator.com/item?id=13098730</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.00625:13098730</guid><pubDate>Sun, 04 Dec 2016 04:02:43 GMT</pubDate><description>&lt;p&gt;&amp;quot;We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch. This white paper argues for using RTS games as a benchmark for AI research, and describes the design and components of TorchCraft.&amp;quot; Via link in the README https://arxiv.org/abs/1611.00625&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.00625/"&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>
