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Fuzzy inference systems optimization By reinforcement learning

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.


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