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Papers in this group

  1. 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…
  2. 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…
  3. 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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