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 multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller (GRFLC), can also learn fuzzy logic control rules even when only weak information such as a binary target of ''success'' or ''failure'' signal is available. In this paper, the adaptive heuristic critic (AHC) algorithm of Barto et al. is extended to include 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 applied to a cart-pole balancing problem via digital simulations.