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  Imitation and Reinforcement Learning

Kober, J., & Peters, J. (2010). Imitation and Reinforcement Learning. IEEE Robotics and Automation Magazine, 17(2), 55-62. doi:10.1109/MRA.2010.936952.

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Kober, J1, 2, Author              
Peters, J1, 2, Author              
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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In this article, we present both novel learning algorithms and experiments using the dynamical system MPs. As such, we describe this MP representation in a way that it is straightforward to reproduce. We review an appropriate imitation learning method, i.e., locally weighted regression, and show how this method can be used both for initializing RL tasks as well as for modifying the start-up phase in a rhythmic task. We also show our current best-suited RL algorithm for this framework, i.e., PoWER. We present two complex motor tasks, i.e., ball-in-a-cup and ball paddling, learned on a real, physical Barrett WAM, using the methods presented in this article. Of particular interest is the ball-paddling application, as it requires a combination of both rhythmic and discrete dynamical systems MPs during the start-up phase to achieve a particular task.

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 Dates: 2010-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/MRA.2010.936952
BibTex Citekey: 6803
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Title: IEEE Robotics and Automation Magazine
Source Genre: Journal
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Pages: - Volume / Issue: 17 (2) Sequence Number: - Start / End Page: 55 - 62 Identifier: -