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  Reinforcement Learning by Relative Entropy Policy Search

Peters, J., Mülling, K., & Altun, Y. (2010). Reinforcement Learning by Relative Entropy Policy Search. In 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010) (pp. 69).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF56-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-B0AD-4
Genre: Meeting Abstract

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 Creators:
Peters, J1, 2, Author              
Mülling, K1, 2, Author              
Altun, Y1, 2, Author              
Affiliations:
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: Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature convergence and implausible solutions. As first suggested in the context of covariant policy gradients, many of these problems may be addressed by constraining the information loss. In this book chapter, we continue this path of reasoning and suggest the Relative Entropy Policy Search (REPS) method. The resulting method differs significantly from previous policy gradient approaches and yields an exact update step. It works well on typical reinforcement learning benchmark problems. We will also present a real-world applications where a robot employs REPS to learn how to return balls in a game of table tennis.

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 Dates: 2010-07
 Publication Status: Published online
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 Identifiers: BibTex Citekey: 6746
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Title: 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)
Place of Event: Chamonix, France
Start-/End Date: 2010-07-04 - 2010-07-09

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Title: 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 69 Identifier: -