skip to main content
10.1145/2330163.2330195acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

HyperNEAT-GGP: a hyperNEAT-based atari general game player

Published:07 July 2012Publication History

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.

References

  1. M. Campbell, A. J. H. Jr., and F. hsiung Hsu. Deep blue. Artif. Intell., 134(1-2):57--83, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. Clune, B. E. Beckmann, C. Ofria, and R. T. Pennock. Evolving coordinated quadruped gaits with the hyperneat generative encoding. In Proceedings of the Eleventh conference on Congress on Evolutionary Computation, CEC'09, pages 2764--2771, Piscataway, NJ, USA, 2009. IEEE Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. B. D'Ambrosio and K. O. Stanley. Generative encoding for multiagent learning. In GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 819--826, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. Diuk, A. Cohen, and M. L. Littman. An object-oriented representation for efficient reinforcement learning. In Proceedings of 25th International Conference on Machine Learning (ICML), pages 240--247, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Edgers. Atari and the deep history of video games. http://www.boston.com/bostonglobe/ideas/articles/2009/03/08/a_talk_with_nick_montfort/.Google ScholarGoogle Scholar
  6. D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. A. Kalyanpur, A. Lally, J. W. Murdock, E. Nyberg, J. Prager, N. Schlaefer, and C. Welty. Building Watson: An Overview of the DeepQA Project. AI Magazine, 31(3), 2010.Google ScholarGoogle Scholar
  7. J. Gauci and K. O. Stanley. A case study on the critical role of geometric regularity in machine learning. In Proceedings of the 23rd National Conference on Artificial Intelligence (AAAI), 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Genesereth and N. Love. General game playing: Overview of the aaai competition. AI Magazine, 26:62--72, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. M. Lucas. Ms pac-man competition (screen capture mode). http://dces.essex.ac.uk/staff/sml/pacman/CIG2011Results.html.Google ScholarGoogle Scholar
  10. S. M. Lucas. Ms pac-man competition. SIGEVOlution, 2(4):37--38, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Y. Naddaf. Game-independent ai agents for playing atari 2600 console games. Master's thesis, University of Alberta, 2010.Google ScholarGoogle Scholar
  12. M. Parker and B. Bryant. Backpropagation without human supervision for visual control in quake ii. Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Games (CIG'09), pages 287--293, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99--127, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. Stone and R. S. Sutton. Scaling reinforcement learning toward RoboCup soccer. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 537--544. Morgan Kaufmann, San Francisco, CA, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. P. Stone, R. S. Sutton, and G. Kuhlmann. Reinforcement learning for RoboCup-soccer keepaway. Adaptive Behavior, 13(3):165--188, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  16. R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. IEEE Transactions on Neural Networks, 9(5):1054--1054, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. G. Tesauro. Td-gammon, a self-teaching backgammon program, achieves master-level play. Neural Comput., 6:215--219, March 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. Verbancsics and K. O. Stanley. Evolving static representations for task transfer. J. Mach. Learn. Res., 11:1737--1769, August 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. HyperNEAT-GGP: a hyperNEAT-based atari general game player

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation
      July 2012
      1396 pages
      ISBN:9781450311779
      DOI:10.1145/2330163

      Copyright © 2012 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 July 2012

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate1,669of4,410submissions,38%

      Upcoming Conference

      GECCO '24
      Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      Melbourne , VIC , Australia

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader