<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1807.01672/</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/1807.01672/feed.xml" rel="self" type="application/rss+xml" /><item><title>Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial Optimization — mentioned by kbeguir</title><link>https://news.ycombinator.com/item?id=21670415</link><guid isPermaLink="false">urn:hackerpapers:mention:1807.01672:21670415</guid><pubDate>Sat, 30 Nov 2019 15:48:26 GMT</pubDate><description>&lt;p&gt;Hi everyone, Karim the Co-founder &amp;amp; CEO of InstaDeep here, first thank you for the interest and comments we did not expect we would appear in HN so quickly :) We’ve reviewed carefully the different points made, comments below: “This is an unsolved problem… Yes, this is the point. It&amp;#x27;s NP-Hard”: &amp;quot;We harness the power of advanced AI and deliver results in less than 24 hours...&amp;quot;, which is a red flag” InstaDeep has built credibility in AI circles by innovating in Machine Learning and Reinforcement Learning (RL) specifically on how to find good solutions to NP-Hard problems with AI. For example…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1807.01672/"&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>
