Abstract: This study deals with avoider obstacle behaviour of a mobile robot in unknown environment. The use of fuzzy logic allows to provide universal rules for avoiding obstacles, and on other hand it gives assurance for a predicted and reliable behaviour of the robot. By lack of reference trajectory, it was urged to use one of reinforcement learning method, fuzzy Q-learning which allows to take into account continual state spaces and actions. In this study, an adaptation method was proposed of fuzzy linguistic rules: after the stage of rules extraction from a fuzzy inference system of fixed structure, we provide a methodology for parameter tuning of the fuzzy sets in terms of the robot inertia without affecting the conclusion rules.