New feature selection method for reinforcement learning: conditional mutual information reveals implicit state-reward dependency (情報論的学習理論と機械学習)
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概要
- 論文の詳細を見る
Model-free reinforcement learning (RL) is a machine learning approach to decision making in unknown environment. However, real-world RL tasks often involve high-dimensional state space, and then standard RL methods do not perform well. In this paper, we propose a new feature selection framework for coping with high dimensionality. Our proposed framework adopts conditional mutual information between state and return sequences as a feature selection criterion, allowing the evaluation of implicit state-reward dependency. The conditional mutual information is approximated by a least-squares method, which results in a computationally efficient feature selection procedure. The usefulness of the proposed method is demonstrated on simulated mobile-robot navigation experiments.
- 社団法人電子情報通信学会の論文
- 2010-06-07
著者
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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