<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>reinforcment learning on Tom Roth</title><link>https://tomroth.dev/tags/reinforcment-learning/</link><description>Recent content in reinforcment learning on Tom Roth</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Mon, 08 Oct 2018 00:00:00 +0000</lastBuildDate><atom:link href="https://tomroth.dev/tags/reinforcment-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Sarsa, expected sarsa and Q-learning on the OpenAI taxi environment</title><link>https://tomroth.dev/sarsa-qlearning/</link><pubDate>Mon, 08 Oct 2018 00:00:00 +0000</pubDate><guid>https://tomroth.dev/sarsa-qlearning/</guid><description>In this post, we&amp;rsquo;ll see how three commonly-used reinforcement algorithms - sarsa, expected sarsa and q-learning - stack up on the OpenAI Gym Taxi (v2) environment.
Note: this post assumes that the reader is familiar with basic RL concepts. A good resource for learning these is the textbook by Sutton and Barto (2018), which is freely available online.
Sarsa, Expected Sarsa and Q-Learning Link to heading So, what are these algorithms?</description></item></channel></rss>