<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Behaviour Suite for Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1908.03568/</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/1908.03568/feed.xml" rel="self" type="application/rss+xml" /><item><title>Behaviour Suite for Reinforcement Learning — mentioned by yd218</title><link>https://news.ycombinator.com/item?id=20709742</link><guid isPermaLink="false">urn:hackerpapers:mention:1908.03568:20709742</guid><pubDate>Thu, 15 Aug 2019 21:15:53 GMT</pubDate><description>&lt;p&gt;You may be interested in our new Behaviour Suite for Reinforcement Learning project at https://github.com/deepmind/bsuite. This is an attempt at defining a set of minimal but meaningful benchmark RL experiments. In each experiment, we typically evaluate over a range of environment parameters and compute an aggregate score. See https://arxiv.org/abs/1908.03568 for more details. We also include a simple starter RL codebase, and examples of evaluating agents defined in other RL frameworks.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1908.03568/"&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>
