<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Multi-Agent on Jungbae Park</title><link>https://jungbaepark.github.io/blog/tags/multi-agent/</link><description>Recent content in Multi-Agent on Jungbae Park</description><generator>Hugo -- 0.152.2</generator><language>en-us</language><lastBuildDate>Tue, 15 Nov 2016 00:00:00 +0000</lastBuildDate><atom:link href="https://jungbaepark.github.io/blog/tags/multi-agent/index.xml" rel="self" type="application/rss+xml"/><item><title>Multi-Agent Cognitive Policy Learning through Competition</title><link>https://jungbaepark.github.io/blog/projects/multi-agent-reinforcement-learning/</link><pubDate>Tue, 15 Nov 2016 00:00:00 +0000</pubDate><guid>https://jungbaepark.github.io/blog/projects/multi-agent-reinforcement-learning/</guid><description>Undergraduate research at KAIST on multi-agent cognitive policy learning through competitive reinforcement learning, demonstrating emergence of complex behaviors. Won Best Paper Award at 2016 KIIS Conference.</description></item></channel></rss>