FUZZY INFERENCE SYSTEMS OPTIMIZATION BY REINFORCEMENT LEARNING
Mohamed Boumehraz, Kamel Benmahammed, M.L. Hadjili, Vincent Wertz · Courrier du Savoir (Universite de Biskra) · 2001
Fuzzy rules for control can be effectively tuned via reinforcement learning.Reinforcement learning is a weak learning method wich only requires information on the succes or failure of the control application.In this paper a reinforcement learning method is used to tune on line the conclusion part of fuzzy inference system rules.The fuzzy rules are tuned in order to maximize the return function .To illustrate its effectivness, the learning method is applied to the well known Cart-Pole balancing system problem.The results obtained show significant improvements of the speed of learning.