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Abstract: A major challenge in reinforcement learning research is to extend methods that have worked well on discrete, short-range, low-dimensional problems to continuous, high-diameter, high-dimensional problems, such as robot navigation using high-resolution sensors. Self-Organizing Distinctivestate Abstraction (SODA) is a new, generic method by which a robot in a continuous world can better learn to navigate by learning a set of high-level features and building temporally-extended actions to... (Update)
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BibTeX entry: (Update)
@misc{ state-developing,
author = "Self-Organizing Distinctive State",
title = "Developing Navigation Behavior Through",
url = "citeseer.ist.psu.edu/748580.html" }
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