An Improved Fuzzy Neural Network for Reinforcement Learning
Takashi Kuremoto, Hiroki Matsusaka, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi · 2019
Reinforcement learning (RL) plays an important role in artificial intelligent (AI) realization. It is a trial-and-error machine learning algorithm for agent's adaptive behavior acquisition in unknown environments. In this paper, an improved fuzzy neural network (iFNN) is proposed with shortcut connection concept for RL. iFNN is based on a self-organizing fuzzy neural network (SOFNN) which is a data-driven fuzzy inference system for RL, and iFNN changes the structure of SOFNN by adding the input vector to units of the middle layer, i.e., fuzzy rules. Furthermore, iFNN is also adopted into multi-layered fuzzy neural network (MLFNN) which is a variation of SOFNN with a deep structure. Goal-navigated exploration experiment results showed the effectiveness of the proposed iFNN.