Closed-Loop Learning of Visual Control Policies

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Closed-Loop Learning of Visual Control Policies
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 prob...
Sébastien Jodogne, Justus H. Piater
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JAIR
Authors Sébastien Jodogne, Justus H. Piater
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