<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bellman on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/bellman/</link><description>Recent content in Bellman on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 13:02:29 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/bellman/index.xml" rel="self" type="application/rss+xml"/><item><title>Dynamic Programming</title><link>https://abaj.ai/wiki/ccs/programming/paradigms/dynamic/</link><pubDate>Fri, 10 Jul 2026 13:02:51 +1000</pubDate><guid>https://abaj.ai/wiki/ccs/programming/paradigms/dynamic/</guid><description>&lt;p>dynamic programming is two things wearing one name. to bellman it was a mathematical theory of &lt;em>multistage decision processes&lt;/em> — sibling to &lt;a
 href="https://abaj.ai/wiki/ccs/programming/paradigms/linear/"
 
 
>linear programming&lt;/a> in the &amp;ldquo;programming means planning&amp;rdquo; sense.&lt;sup id="fnref:1">&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref">1&lt;/a>&lt;/sup>&lt;span class="margin-note" data-note="bellman chose the name partly to hide the mathematics from a defence secretary who hated the word research; the story is in the footnote">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
&lt;/span>

to a computer scientist it is a technique: solve a problem by combining solutions to subproblems, and never solve the same subproblem twice (Cormen, Thomas H. and Leiserson, Charles E. and Rivest, Ronald L. and Stein, Clifford, 2009). the two are the same idea at different altitudes, and this page covers both. worked implementations also live in this &lt;a
 href="https://github.com/abaj8494/dynamic-programming"
 
 
 class="link--external" target="_blank" rel="noreferrer"
 
>github repo&lt;/a>.&lt;/p></description></item><item><title>Q-Learning</title><link>https://abaj.ai/wiki/ml/reinforcement-learning/q-learning/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/reinforcement-learning/q-learning/</guid><description>&lt;p>q-learning is the algorithm that made reinforcement learning feel inevitable: interact with an unknown world, nudge a table of numbers after every step, and the table converges to the value of &lt;em>optimal&lt;/em> behaviour — even while you behave suboptimally the entire time.&lt;span class="margin-note" data-note="watkins introduced it in his 1989 phd thesis; watkins and dayan supplied the convergence proof in 1992">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
&lt;/span>

everything runs on one line of arithmetic, and the rest of this page is the machinery needed to say precisely why that line works. the canonical reference for all of it is sutton &amp;amp; barto&amp;rsquo;s &lt;em>reinforcement learning: an introduction&lt;/em>, free at &lt;a
 href="http://incompleteideas.net/book/the-book-2nd.html"
 
 
 class="link--external" target="_blank" rel="noreferrer"
 
>http://incompleteideas.net/book/the-book-2nd.html&lt;/a>.&lt;/p></description></item></channel></rss>