COMPACT FUZZY Q LEARNING FOR AUTONOMOUS MOBILE ROBOT NAVIGATION
Sulistijono, Indra Adjie
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Robot which does complex task needs learning capability. Q learning is popular reinforcement learning method because it has off-line policy characteristic and simple algorithm. But it only suitable in discrete state and action. By using Fuzzy Q Learning (FQL), continuous state and action can be handled too. Unfortunately, it’s not easy to implement FQL algorithm to real robot because its complexity and robot’s limited memory capacity. In this research, Compact FQL (CFQL) algorithm is proposed to solve those weaknesses. By using CFQL, robot still can accomplish its task in autonomous navigation although its performance is not as good as robot using FQL.