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- W3101639353 abstract "In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical Car on the Hill control problem." @default.
- W3101639353 created "2020-11-23" @default.
- W3101639353 creator A5020592313 @default.
- W3101639353 creator A5024515193 @default.
- W3101639353 date "2007-03-26" @default.
- W3101639353 modified "2023-09-26" @default.
- W3101639353 title "Closed-Loop Learning of Visual Control Policies" @default.
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- W3101639353 doi "https://doi.org/10.1613/jair.2110" @default.
- W3101639353 hasPublicationYear "2007" @default.
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