抄録
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.
