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HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player.
Matthew
Hausknecht, Piyush Khandelwal, Risto
Miikkulainen, and Peter Stone.
In Genetic and Evolutionary Computation
Conference (GECCO), July 2012.
[PDF]880.9kB [postscript]3.3MB
This paper considers the challenge of enabling agents tolearn with as little domain-specific knowledge as possible. The maincontribution is HyperNEAT-GGP, a HyperNEAT-based General Game Playingapproach to Atari games. By leveraging the geometric regularitiespresent in the Atari game screen, HyperNEAT effectively evolvespolicies for playing two different Atari games, Asterix and Freeway.Results show that HyperNEAT-GGP outperforms existing benchmarks onthese games. HyperNEAT-GGP represents a step towards the ambitiousgoal of creating an agent capable of learning and seamlesslytransitioning between many different tasks.
@InProceedings{GECCO12-Hausknecht,
title={HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player},
author={Matthew Hausknecht and Piyush Khandelwal and Risto Miikkulainen and Peter Stone},
booktitle={Genetic and Evolutionary Computation Conference (GECCO)},
location={Philadelphia, Pennsylvania, USA},
month={July},
url="http://www.cs.utexas.edu/users/ai-lab/pub-view.php?PubID=127155",
year={2012},
abstract={This paper considers the challenge of enabling agents to
learn with as little domain-specific knowledge as possible. The main
contribution is HyperNEAT-GGP, a HyperNEAT-based General Game Playing
approach to Atari games. By leveraging the geometric regularities
present in the Atari game screen, HyperNEAT effectively evolves
policies for playing two different Atari games, Asterix and Freeway.
Results show that HyperNEAT-GGP outperforms existing benchmarks on
these games. HyperNEAT-GGP represents a step towards the ambitious
goal of creating an agent capable of learning and seamlessly
transitioning between many different tasks.},
}
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