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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1707.06307/</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/1707.06307/feed.xml" rel="self" type="application/rss+xml" /><item><title>Reinforcement Learning Produces Dominant Strategies for the Iterated Prisoner's Dilemma — mentioned by drvinceknight</title><link>https://news.ycombinator.com/item?id=15124466</link><guid isPermaLink="false">urn:hackerpapers:mention:1707.06307:15124466</guid><pubDate>Tue, 29 Aug 2017 14:25:06 GMT</pubDate><description>&lt;p&gt;The Axelrod library is a Python implementation of the Prisoner&amp;#x27;s dilemma (tournaments, evolutionary dynamics etc) with over 200 strategies. It can also be used to study other games (http://axelrod.readthedocs.io/en/stable/tutorials/advanced/g...) so it would be straightforward to study the snowdrift game using it :) Re some of the other comments about Robert Axelrod&amp;#x27;s tournament and evolution: - As part of the project, the Axelrod library developers have written Python wrappers for the original Fortran code (https://github.com/Axelrod-Python/axelrod-fortran) - Have used reinforcement learning…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1707.06307/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.GT</category></item></channel></rss>
