Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

Abstract

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. (1) The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, matches well with the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials as well as consumed CPU time are reduced considerably as compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. The proposed SEFC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SEFC have been verified from this problem and from the comparisons with traditional GA-based fuzzy systems.