<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Reinforcement Learning |</title><link>https://aroraanshu26.github.io/tags/reinforcement-learning/</link><atom:link href="https://aroraanshu26.github.io/tags/reinforcement-learning/index.xml" rel="self" type="application/rss+xml"/><description>Reinforcement Learning</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 30 Apr 2026 00:00:00 +0000</lastBuildDate><image><url>https://aroraanshu26.github.io/media/icon_hu_da05098ef60dc2e7.png</url><title>Reinforcement Learning</title><link>https://aroraanshu26.github.io/tags/reinforcement-learning/</link></image><item><title>Tracing the Roots: Trust Region Policy Optimization</title><link>https://aroraanshu26.github.io/publications/ds608/</link><pubDate>Thu, 30 Apr 2026 00:00:00 +0000</pubDate><guid>https://aroraanshu26.github.io/publications/ds608/</guid><description/></item><item><title>One-shot Clustering for Policy Evaluation in Federated TD Learning with LFA</title><link>https://aroraanshu26.github.io/publications/fedrl/</link><pubDate>Fri, 07 Nov 2025 00:00:00 +0000</pubDate><guid>https://aroraanshu26.github.io/publications/fedrl/</guid><description/></item><item><title>Hands-on Reinforcement Learning</title><link>https://aroraanshu26.github.io/projects/scikit/</link><pubDate>Mon, 03 Jun 2024 00:00:00 +0000</pubDate><guid>https://aroraanshu26.github.io/projects/scikit/</guid><description>&lt;p&gt;This is my exploratory project in Reinforcement Learning, where I have implemented common learning algorithms such as Q-learning, to play the mountain car game. I have also implemented different greedy, epsilon greedy, UCB sampling strategy, and have plotted their regret versus number of samples.&lt;/p&gt;</description></item></channel></rss>