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

7 Scopus citations

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. © 2009 Springer Berlin Heidelberg.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages765-774
Number of pages10
DOIs
StatePublished - Nov 30 2009
Externally publishedYes

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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