Learning fuzzy control by evolutionary and advantage reinforcements

Abstract

In this paper, evolutionary and dynamic programming-based reinforcement learning techniques are combined to form an unsupervised learning scheme for designing autonomous optimal fuzzy logic control systems. A ‘‘messy genetic algorithm’’ and an ‘‘advantage learning’’ scheme are first compared as reinforcement learning paradigms. The messy genetic algorithm enables flexible coding of a fuzzy structure for global optimization, resulting in a coarsely optimized feedforward-type neurofuzzy structure. Local pruning and fine tuning of the neurofuzzy system is then achieved effectively by advantage learning by directly interacting with the environment without the use of a supervisor. The methodology is illustrated and tested in detail through application to two nonlinear control systems.