Fusing learning strategies to learn various tasks with single configuration (ニューロコンピューティング)
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概要
- 論文の詳細を見る
This paper proposes a method to fuse learning strategies (LSs) in reinforcement learning framework. Generally, we need to choose a suitable LS for each task respectively. In contrast, the proposed method automates this selection by fusing LSs. The LSs fused in this paper includes a transfer learning, a hierarchical RL, and a model based RL. The proposed method has a wide applicability. When the method is applied to a motion learning task, such as a crawling task, the performance of motion may be improved compared to an agent with a single LS. The method also can be applied to a navigation task by hierarchically combining already learned motions, such as a crawling and a turning. This paper demonstrates a maze task of a humanoid robot where the robot learns not only a path to goal, but also a crawling and a turning motions.
- 2011-02-28
著者
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OGASAWARA Tsukasa
Graduate School of Information Science, Nara Institute of Science and Technology
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Ogasawara Tsukasa
Graduate School Of Information Science Nara Institute Of Science And Technology
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YAMAGUCHI Akihiko
Graduate School of Information Science, Nara Institute of Science and Technology
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TAKAMATSU Jun
Graduate School of Information Science, Nara Institute of Science and Technology
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Takamatsu Jun
Graduate School Of Information Science Nara Institute Of Science And Technology
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Yamaguchi Akihiko
Graduate School Of Information Science Nara Institute Of Science And Technology
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