Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems

The authors propose a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. RNN-FLCS is best applied to learning environments where obtaining exact training data is expensive. It is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLCs), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller implements a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, the RNN-FLCs can construct a fuzzy logic control system automatically and dynamically through a reward-penalty signal or through very simple fuzzy information feedback. Structure learning and parameter learning are performed simultaneously in the two NN-FLCs. Simulation results are presented