Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation
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概要
- 論文の詳細を見る
Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers in observed rewards. In this paper, we propose an alternative method that employs the absolute loss for enhancing robustness and reliability. The proposed method is formulated as a linear programming problem which can be solved efficiently by standard optimization software, so the computational advantage is not sacrificed for gaining robustness and reliability. We demonstrate the usefulness of the proposed approach through a simulated robot-control task.
- （社）電子情報通信学会の論文
- 2010-09-01
著者
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
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KASHIMA Hisashi
Department of Mathematical Informatics, the University of Tokyo
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MORIMURA Tetsuro
IBM Research - Tokyo
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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Kashima Hisashi
Department Of Mathematical Informatics Graduate School Of Information Science And Technology The Uni
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Kashima Hisashi
Department Of Mathematical Informatics The University Of Tokyo
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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Sugiyama Masashi
Department of Applied Chemistry, Yamanashi University
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