Papers in this group
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As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without numerous high-cost learning trials. One promising approach to reducing…
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Wepresent a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and…
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In order for learning agents to be useful to non-technical users, it is important to be able to teach agents how to per- form new tasks using simple communication methods. We begin this paper by describing a framework we recently de- veloped called…
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