<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Monte Carlo Tree Search on Matt Down</title><link>https://mattdown.ai/tags/monte-carlo-tree-search/</link><description>Recent content in Monte Carlo Tree Search on Matt Down</description><generator>Hugo</generator><language>en-us</language><copyright>Matt Down</copyright><lastBuildDate>Wed, 17 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://mattdown.ai/tags/monte-carlo-tree-search/index.xml" rel="self" type="application/rss+xml"/><item><title>Interactive Monte Carlo Tree Search Demo</title><link>https://mattdown.ai/posts/mcts/</link><pubDate>Wed, 17 Jun 2026 00:00:00 +0000</pubDate><guid>https://mattdown.ai/posts/mcts/</guid><description>&lt;h2 id="intro"&gt;Intro&lt;/h2&gt;
&lt;p&gt;Monte Carlo Tree Search (MCTS) is the famous tree-search planning algorithm used in Google DeepMind&amp;rsquo;s AlphaGo as well as many other
applications. The algorithm itself is relatively simple and should be seen maybe more as a planning algorithm than a learning one.&lt;/p&gt;
&lt;p&gt;At a high level, how does MCTS work? Instead of exhaustively searching all possibilities, MCTS searches the state tree in a
principled way that prioritises exploration of the most promising paths within that tree.&lt;/p&gt;</description></item></channel></rss>