Using localization and factorization to reduce the complexity of reinforcement learning

Peter Sunehag*, Marcus Hutter

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)

    Abstract

    General reinforcement learning is a powerful framework for artificial intelligence that has seen much theoretical progress since introduced fifteen years ago.We have previously provided guarantees for cases with finitely many possible environments. Though the results are the best possible in general, a linear dependence on the size of the hypothesis class renders them impractical. However, we dramatically improved on these by introducing the concept of environments generated by combining laws. The bounds are then linear in the number of laws needed to generate the environment class. This number is identified as a natural complexity measure for classes of environments. The individual law might only predict some feature (factorization) and only in some contexts (localization). We here extend previous deterministic results to the important stochastic setting.

    Original languageEnglish
    Title of host publicationArtificial General Intelligence - 8th International Conference, AGI 2015, Proceedings
    EditorsAlexey Potapov, Jordi Bieger, Ben Goertzel
    PublisherSpringer Verlag
    Pages177-186
    Number of pages10
    ISBN (Print)9783319213644
    DOIs
    Publication statusPublished - 2015
    Event8th International Conference on Artificial General Intelligence, AGI 2015 - Berlin, Germany
    Duration: 22 Jul 201525 Jul 2015

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9205
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference8th International Conference on Artificial General Intelligence, AGI 2015
    Country/TerritoryGermany
    CityBerlin
    Period22/07/1525/07/15

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