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.
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.
针对直觉模糊粗糙逻辑（IFRL）推理的规则库检验问题，提出了IFRL规则库的互作用性检验方法。
To the rule-bases checking issue with intuitionistic fuzzy rough logical(IFRL) reasoning, an interactivities checking approach to IFRL rule-bases is proposed.
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