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A Neuroevolution Approach to General Atari Game Playing (2013)
Author: Matthew Hausknecht
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
Formerly affiliated 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
Faculty
pstone [at] cs utexas edu
Projects
Learning Strategic Behavior in Sequential Decision Tasks
2009 - 2014
Publications
A Neuroevolution Approach to General Atari Game Playing
2013
Matthew Hausknecht, Joel Lehman, Risto Miikkulainen, and Peter Stone,
IEEE Transactions on Computational Intelligence and AI in Games
(2013).
Related Areas
General Game Playing
Machine Learning
Neuroevolution
Reinforcement Learning
Labs
Learning Agents
Neural Networks