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HyperNEAT-GGP: A HyperNEAT-based Atari General Game Player (2012)
Matthew Hausknecht
,
Piyush Khandelwal
,
Risto Miikkulainen
,
Peter Stone
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.
View:
PDF
Citation:
In
Genetic and Evolutionary Computation Conference (GECCO) 2012
2012.
Bibtex:
@inproceedings{hausknecht:gecco12, 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) 2012}, url="http://www.cs.utexas.edu/users/ai-lab?hausknecht:gecco12", year={2012} }
People
Matthew Hausknecht
Formerly affiliated Collaborator
mhauskn [at] cs utexas edu
Piyush Khandelwal
Ph.D. Alumni
piyushk [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
Areas of Interest
Evolutionary Computation
Game Playing
Neuroevolution
Labs
Neural Networks
Learning Agents