The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

We present a method for reducing the effort required to compute policies for tasks based on solutions to previously solved tasks. The key idea is to use a learned intermediate policy based on local features to create an initial policy for the new task. In order to further improve this initial policy, we developed a form of generalized policy iteration. We achieve a substantial reduction in computation needed to find policies when previous experience is available.

Notes

Associated Center(s) / Consortia: Center for the Foundations of Robotics

Associated Lab(s) / Group(s): Planning and Autonomy Lab

Associated Project(s): Learning Locomotion

Text Reference

Martin Stolle and Chris Atkeson, "Knowledge transfer using local features," Proceedings of the IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning, 2007.

BibTeX Reference

@inproceedings{Stolle_2007_6096,
   author = "Martin Stolle and Chris Atkeson",
   title = "Knowledge transfer using local features",
   booktitle = "Proceedings of the IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning",
   year = "2007",
}

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