Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed. The genetic algorithm (GA) adopted 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 (SE-FC) 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 SE-FC design method has been applied to the cart-pole balancing system. Efficiency and superiority of the proposed SE-FC have been verified from this problem and from comparisons with the traditional GA-based fuzzy systems