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  Using reward-weighted imitation for robot Reinforcement Learning

Peters, J., & Kober, J. (2009). Using reward-weighted imitation for robot Reinforcement Learning. In 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (pp. 226-232). Piscataway, NJ, USA: IEEE Service Center.

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Peters, J1, 2, Author              
Kober, 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: Reinforcement Learning is an essential ability for robots to learn new motor skills. Nevertheless, few methods scale into the domain of anthropomorphic robotics. In order to improve in terms of efficiency, the problem is reduced onto reward-weighted imitation. By doing so, we are able to generate a framework for policy learning which both unifies previous reinforcement learning approaches and allows the derivation of novel algorithms. We show our two most relevant applications both for motor primitive learning (e.g., a complex Ball-in-a-Cup task using a real Barrett WAM robot arm) and learning task-space control.

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 Dates: 2009-05
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.1109/ADPRL.2009.4927549
BibTex Citekey: 5659
 Degree: -

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Title: 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Place of Event: Nashville, TN, USA
Start-/End Date: 2009-03-30 - 2009-04-02

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Title: 2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 226 - 232 Identifier: ISBN: 978-1-4244-2761-1