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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1801.01290/</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/1801.01290/feed.xml" rel="self" type="application/rss+xml" /><item><title>Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor — mentioned by programjames</title><link>https://news.ycombinator.com/item?id=40756448</link><guid isPermaLink="false">urn:hackerpapers:mention:1801.01290:40756448</guid><pubDate>Sat, 22 Jun 2024 05:10:56 GMT</pubDate><description>&lt;p&gt;Great paper! There are some similar ideas to this in game theory and reinforcement learning (RL): [1]: Thermodynamic Game Theory: https://adamilab.msu.edu/wp-content/uploads/AdamiHintze2018.... [2]: piKL - KL-regularized RL: https://arxiv.org/abs/2112.07544 [3]: Soft-Actor Critic - Entropy-regularized RL: https://arxiv.org/abs/1801.01290 [4]: &amp;quot;Soft&amp;quot; (Boltzmann) Q-learning = Entropy-regularized policy gradients: https://arxiv.org/abs/1704.06440&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1801.01290/"&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>
