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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1912.03513/</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/1912.03513/feed.xml" rel="self" type="application/rss+xml" /><item><title>From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions — mentioned by cashsterling</title><link>https://news.ycombinator.com/item?id=35474726</link><guid isPermaLink="false">urn:hackerpapers:mention:1912.03513:35474726</guid><pubDate>Thu, 06 Apr 2023 21:26:38 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;d say RL is an advanced robotics topic. There are some books out there that cover introductory or general RL aspects... after that you&amp;#x27;ll have to dive into papers and specific code bases. Might not be a bad idea to start with a Python-based, practical RL book and move from practical demonstrations towards theory. Although not an RL book, I really like Data-Driven Science and Engineering by Brunton and Kutz: - https://www.databookuw.com/ - https://www.amazon.com/Data-Driven-Science-Engineering-Learn... Steve Brunton also has an awesome youtube channel on dynamic systems, control, and machine…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1912.03513/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
