Fuzzy inference-based reinforcement learning of dynamic recurrent neural networks
Hyo-Byung Jun, Dong-Wook Lee, Dae-Joon Kim, Kwee-Bo Sim · 2002
This paper presents the fuzzy inference-based reinforcement learning algorithm of dynamic recurrent neural network, similar to the psychological learning scheme of the higher animals. The proposed method follows the way linguistic and conceptional expressions have an effect on human's behavior by reasoning reinforcement based on fuzzy rules. The intervals of fuzzy membership functions are found optimally by genetic algorithms. By using the recurrent neural network composed of dynamic neurons as action-generation network, not only the current state but also the past state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.