巴韦利埃表示：“令人感到吃惊的是，靠玩动作游戏提高的概率推理能力并不仅限于游戏，同时也可用于完成与游戏无关的更为鼓噪的任务。”

“What’s surprising in our study is that action games improved probabilistic inference not just for the act of gaming, but for unrelated and rather dull tasks,” Bavelier says.

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论文提出一种模糊强化学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。

In this paper, we propose a fuzzy reinforcement algorithm, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.

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首先，提出一种模糊Q学习算法，通过模糊推理系统将连续的状态空间映射到连续的动作空间，然后通过学习得到一个完整的规则库。

A fuzzy Q learning algorithm is proposed in this dissertation, which map continuous state Spaces to continuous action Spaces by fuzzy inference system and then learn a rule base.

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