A Neuroevolution Approach to General Atari Game Playing

General Game Players are learning algorithms capable of performing many different tasks without needing to be reconfigured, re-programmed, or given task-specific knowledge. The videos below show the results of general game playing algorithms applied to classic Atari 2600 video games.

Demo website

People

Matthew Hausknecht	Former Collaborator	mhauskn [at] cs utexas edu
Joel Lehman	Postdoctoral Alumni	joel [at] cs utexas edu
Risto Miikkulainen	Faculty	risto [at] cs utexas edu
Peter Stone		pstone [at] cs utexas edu

Projects

Learning Strategic Behavior in Sequential Decision Tasks

2009 - 2014

Publications

A Neuroevolution Approach to General Atari Game Playing	Matthew Hausknecht and Joel Lehman and Risto Miikkulainen and Peter Stone	IEEE Transactions on Computational Intelligence and AI in Games, 2013.	2013

Neuroevolution: Harnessing Creativity in AI Model Design	Sebastian Risi, David Ha, Yujin Tang, Risto Miikkulainen	To Appear In , Cambridge, MA, 2025. MIT Press.	2025

Areas of Interest

General Game Playing Machine Learning Neuroevolution Reinforcement Learning