Measuring and optimizing behavioral complexity for evolutionary reinforcement learning

Faustino J. Gomez, Julian Togelius, Juergen Schmidhuber

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Model complexity is key concern to any artificial learning system due its critical impact on generalization. However, EC research has only focused phenotype structural complexity for static problems. For sequential decision tasks, phenotypes that are very similar in structure, can produce radically different behaviors, and the trade-off between fitness and complexity in this context is not clear. In this paper, behavioral complexity is measured explicitly using compression, and used as a separate objective to be optimized (not as an additional regularization term in a scalar fitness), in order to study this trade-off directly.

    Original languageEnglish (US)
    Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
    Pages765-774
    Number of pages10
    EditionPART 2
    DOIs
    StatePublished - Nov 30 2009
    Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
    Duration: Sep 14 2009Sep 17 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 2
    Volume5769 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other19th International Conference on Artificial Neural Networks, ICANN 2009
    CountryCyprus
    CityLimassol
    Period9/14/099/17/09

      Fingerprint

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Gomez, F. J., Togelius, J., & Schmidhuber, J. (2009). Measuring and optimizing behavioral complexity for evolutionary reinforcement learning. In Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings (PART 2 ed., pp. 765-774). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5769 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-04277-5_77