Feature reinforcement learning in practice

Phuong Nguyen*, Peter Sunehag, Marcus Hutter

*Corresponding author for this work

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

    6 Citations (Scopus)

    Abstract

    Following a recent surge in using history-based methods for resolving perceptual aliasing in reinforcement learning, we introduce an algorithm based on the feature reinforcement learning framework called ΦMDP [13]. To create a practical algorithm we devise a stochastic search procedure for a class of context trees based on parallel tempering and a specialized proposal distribution. We provide the first empirical evaluation for ΦMDP. Our proposed algorithm achieves superior performance to the classical U-tree algorithm [20] and the recent active-LZ algorithm [6], and is competitive with MC-AIXI-CTW [29] that maintains a bayesian mixture over all context trees up to a chosen depth. We are encouraged by our ability to compete with this sophisticated method using an algorithm that simply picks one single model, and uses Q-learning on the corresponding MDP. Our ΦMDP algorithm is simpler and consumes less time and memory. These results show promise for our future work on attacking more complex and larger problems.

    Original languageEnglish
    Title of host publicationRecent Advances in Reinforcement Learning - 9th European Workshop, EWRL 2011, Revised Selected Papers
    Pages66-77
    Number of pages12
    DOIs
    Publication statusPublished - 2012
    Event9th European Workshop on Reinforcement Learning, EWRL 2011 - Athens, Greece
    Duration: 9 Sept 201111 Sept 2011

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7188 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th European Workshop on Reinforcement Learning, EWRL 2011
    Country/TerritoryGreece
    CityAthens
    Period9/09/1111/09/11

    Fingerprint

    Dive into the research topics of 'Feature reinforcement learning in practice'. Together they form a unique fingerprint.

    Cite this