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

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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: CFR
Associated lab/group: Planning and Autonomy Lab
Associated project: Learning Locomotion

Text Reference

M. Stolle and C. 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"
}