Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems
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概要
- 論文の詳細を見る
Reinforcement learning (RL) is a flexible framework for learning a decision rule in an unknown environment. However, a large number of samples are often required for finding a useful decision rule. To mitigate this problem, the concept of transfer learning has been employed to utilize knowledge obtained from similar RL tasks. However, most approaches developed so far are useful only in low-dimensional settings. In this paper, we propose a novel transfer learning idea that targets problems with high-dimensional states. Our idea is to transfer knowledge between state factors (e.g., interacting objects) within a single RL task. This allows the agent to learn the system dynamics of the target RL task with fewer data samples. The effectiveness of the proposed method is demonstrated through experiments.
- The Institute of Electronics, Information and Communication Engineersの論文
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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HACHIYA HIROTAKA
Tokyo Institute of Technology
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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SIMM Jaak
Tallinn University of Technology
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