Antecedent Redundancy Exploitation in Fuzzy Rule Interpolation-based Reinforcement Learning
Antecedent Redundancy Exploitation in Fuzzy Rule Interpolation-based Reinforcement Learning
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DOI：
10.1109/aim43001.2020.9158875
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Dávid Vincze;Alex Tóth;M. Niitsuma
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文献类型：
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作者：
Dávid Vincze;Alex Tóth;M. Niitsuma
本文介绍了可以提高 FRIQ 学习（基于模糊规则插值的 Q 学习）机器学习方法中使用的自动知识提取方法效率的新方法。对于解决给定问题，FRIQ-learning强化学习方法能够构建稀疏模糊规则库，而无需像传统模糊控制那样包含所有可能的规则。因此，由于模糊规则插值（FRI），仅保留最重要的规则就足够了。找到对于解决给定问题很重要的特定规则并不是一项简单的任务。已经引入了一些从规则库中删除这些不重要规则的可能策略，但尚未开发出解决规则前因的策略。本文提出的解决方案允许进一步减少这些规则库，从而有助于创建可以直接提取知识的稀疏模糊规则库。由于模糊规则的形式本质上是自描述的，因此规则库的大小是保持此类知识库人类可读的关键。这些方法可能的机电一体化应用包括优化机器人基于行为的控制模型，以及从模型的真实操作知识未知的模型中以模糊规则库格式提取知识，然后可以在机器人控制应用中轻松采用该规则库。
This paper introduces novel methods which could improve the efficiency of the automated knowledge extraction methods used in the FRIQ-learning (Fuzzy Rule Interpolationbased Q-learning) machine learning method. For solving a given problem, the FRIQ-learning reinforcement learning method is capable of constructing a sparse fuzzy rule-base, which does not need to contain all the possible rules as traditional fuzzy control requires. Hence it is sufficient to keep only the most important rules due to Fuzzy Rule Interpolation (FRI). Finding those specific rules which are important to solve the given problem is not a trivial task. Some possible strategies for removing these kinds of unimportant rules from the rule-base have already been introduced, but no strategies addressing the antecedents of the rules have been developed yet. The solutions proposed in this paper allow the further reduction of these rule-bases, thus facilitating the creation of a sparse fuzzy rule-base from which the knowledge can be directly extracted. Since the form of fuzzy rules are inherently self-describing, the size of the rule-base is the key for keeping this kind of knowledge base human-readable. Possible mechatronics applications of these methods include optimizing behaviour-based control models for robotics, and also knowledge extraction in a fuzzy rule-base format from models where the real operating knowledge of the model is not known, which rule-bases then can be easily adopted in robot control applications.