A SELF-LEARNING FUZZY-LOGIC CONTROLLER USING GENETIC ALGORITHMS WITH REINFORCEMENTS

This paper presents a new method for learning a fuzzy logic controller automatically, A reinforcement learning technique is applied to a mul tilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm thr ough reinforcement learning architecture, called a genetic reinforceme nt fuzzy logic controller (GRFLC), can also learn fuzzy logic control rules even when only weak information such as a binary target of ''suc cess'' or ''failure'' signal is available. In this paper, the adaptive heuristic critic (AHC) algorithm of Barto et al. is extended to inclu de a priori control knowledge of human operators, It is shown that the system can solve more concretely a fairly difficult control learning problem, Also demonstrated is the feasibility of the method when appli ed to a cart-pole balancing problem via digital simulations.