2P1-F30 連続な状態行動空間において学習可能なQ-learningの提案
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概要
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This paper proposes the new Q-learning that can learn mapping from continue state spaces to continue action spaces. The proposed method estimates the expectation value of actions on a state by using artificial neural networks, and decides an action according to the distribution of the estimated expectation value. In this paper, we investigate the performance of the proposed method through two types of simple experimentations.
- 一般社団法人日本機械学会の論文