A novel genetic reinforcement learning for nonlinear fuzzy control problems

Unlike a supervise learning, a reinforcement learning problem has only very simple “evaluative” or “critic” information available for learning, rather than “instructive” information. A novel genetic reinforcement learning, called reinforcement sequential-search-based genetic algorithm (R-SSGA), is proposed for solving the nonlinear fuzzy control problems in this paper. Unlike the traditional reinforcement genetic algorithm, the proposed R-SSGA method adopts the sequential-search-based genetic algorithms (SSGA) to tune the fuzzy controller. Therefore, the better chromosomes will be initially generated while the better mutation points will be determined for performing efficient mutation. The adjustable parameters of fuzzy controller are coded as real number components. We formulate a number of time steps before failure occurs as a fitness function. Simulation results have shown that the proposed R-SSGA method converges quickly and minimizes the population size.