<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforce on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/reinforce/</link><description>Recent content in Reinforce on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Thu, 09 Jul 2026 21:02:28 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/reinforce/index.xml" rel="self" type="application/rss+xml"/><item><title>Policy Gradients</title><link>https://abaj.ai/wiki/ml/reinforcement-learning/policy-gradients/</link><pubDate>Thu, 09 Jul 2026 21:02:56 +1000</pubDate><guid>https://abaj.ai/wiki/ml/reinforcement-learning/policy-gradients/</guid><description>&lt;p>value-based methods (&lt;a
 href="https://abaj.ai/wiki/ml/reinforcement-learning/q-learning/"
 
 
>q-learning&lt;/a> and family) learn &lt;em>how good actions are&lt;/em> and act by argmax. policy-gradient methods skip the middleman: parameterise the policy itself, \(\pi_\theta(a \mid s)\), and do gradient ascent on expected return.&lt;span class="margin-note" data-note="the argmax is also the problem: it is undefined over continuous actions and allergic to stochastic policies. differentiation handles both">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
&lt;/span>

the entire family — reinforce, actor-critic, trpo, ppo, and by extension rlhf — rests on one identity, the policy gradient theorem, whose derivation is three lines of calculus and one very good idea. the standard reference is sutton &amp;amp; barto, 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>