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  Using Reward-weighted Regression for Reinforcement Learning of Task Space Control

Peters, J., & Schaal, S. (2007). Using Reward-weighted Regression for Reinforcement Learning of Task Space Control. In 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (pp. 262-267). Piscataway, NJ, USA: IEEE Computer Society.

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Peters, J1, Author              
Schaal, S, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Many robot control problems of practical importance, including task or operational space control, can be reformulated as immediate reward reinforcement learning problems. However, few of the known optimization or reinforcement learning algorithms can be used in online learning control for robots, as they are either prohibitively slow, do not scale to interesting domains of complex robots, or require trying out policies generated by random search, which are infeasible for a physical system. Using a generalization of the EM-base reinforcement learning framework suggested by Dayan amp; Hinton, we reduce the problem of learning with immediate rewards to a reward-weighted regression problem with an adaptive, integrated reward transformation for faster convergence. The resulting algorithm is efficient, learns smoothly without dangerous jumps in solution space, and works well in applications of complex high degree-of-freedom robots.

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 Dates: 2007-04
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/ADPRL.2007.368197
BibTex Citekey: 4724
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Title: IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL 2007)
Place of Event: Honolulu, HI, USA
Start-/End Date: 2007-04-01 - 2007-04-05

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Title: 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 262 - 267 Identifier: ISBN: 1-4244-0706-0