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  Learning Strategies in Table Tennis using Inverse Reinforcement Learning

Mülling, K., Boularias, A., Mohler, B. J., Schölkopf, B., & Peters, J. (2014). Learning Strategies in Table Tennis using Inverse Reinforcement Learning. Biological Cybernetics, 108(5), 603-619. doi:10.1007/s00422-014-0599-1.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-1CFD-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1CFE-3
Genre: Journal Article

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Mülling, K.1, Author              
Boularias, A.1, Author              
Mohler, Betty J2, 3, Author              
Schölkopf, B.1, Author              
Peters, J.1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2Research Group Space and Body Perception, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_2528693              
3Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Learning a complex task such as table tennis is a challenging problem for both robots and humans. Even after acquiring the necessary motor skills, a strategy is needed to choose where and how to return the ball to the opponent’s court in order to win the game. The data-driven identification of basic strategies in interactive tasks, such as table tennis, is a largely unexplored problem. In this paper, we suggest a computational model for representing and inferring strategies, based on a Markov decision problem, where the reward function models the goal of the task as well as the strategic information. We show how this reward function can be discovered from demonstrations of table tennis matches using model-free inverse reinforcement learning. The resulting framework allows to identify basic elements on which the selection of striking movements is based. We tested our approach on data collected from players with different playing styles and under different playing conditions. The estimated reward function was able to capture expert-specific strategic information that sufficed to distinguish the expert among players with different skill levels as well as different playing styles.

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 Dates: 2014-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1007/s00422-014-0599-1
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Title: Biological Cybernetics
Source Genre: Journal
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Pages: - Volume / Issue: 108 (5) Sequence Number: - Start / End Page: 603 - 619 Identifier: -