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  Policy Learning: A Unified Perspective with Applications in Robotics

Peters, J., Kober, J., & Nguyen-Tuong, D. (2008). Policy Learning: A Unified Perspective with Applications in Robotics. Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008), 220-228.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C669-B Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C66A-9
Genre: Conference Paper

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 Creators:
Peters, J1, 2, Author           
Kober, J1, Author           
Nguyen-Tuong, D1, Author           
Girgin, Editor
S., Editor
Loth, M., Editor
Munos, R., Editor
Preux, P., Editor
Ryabko, D., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Policy Learning approaches are among the best suited methods for high-dimensional, continuous control systems such as anthropomorphic robot arms and humanoid robots. In this paper, we show two contributions: firstly, we show a unified perspective which allows us to derive several policy learning algorithms from a common point of view, i.e, policy gradient algorithms, natural-gradient algorithms and EM-like policy learning. Secondly, we present several applications to both robot motor primitive learning as well as to robot control in task space. Results both from simulation and several different real robots are shown.

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 Dates: 2008-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://ewrl08.futurs.inria.fr/
DOI: 10.1007/978-3-540-89722-4_17
BibTex Citekey: 5177
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Title: 8th European Workshop on Reinforcement Learning
Place of Event: Villeneuve d‘Ascq, France
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Title: Recent Advances in Reinforcement Learning: 8th European Workshop (EWRL 2008)
Source Genre: Journal
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Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 220 - 228 Identifier: -