A Reinforcement Learning System with Multi-Layered Fuzzy Neural Network
Takashi Kuremoto, Hiroki Matsusaka, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi · 2017
To define the state of unknown environment for intelligent agent or autonomous robot, many methods such as neural networks, linear models, decision trees, etc have been proposed in the study of reinforcement learning field. In this paper, a self-organized fuzzy neural network with multiple fuzzy inference layers (MLFNN) is proposed. The deeper fuzzy inference layer includes fuzzy membership functions and fuzzy rules as same as the normal fuzzy neural networks, however, it extracts abstract states of the input data. Goal-navigated exploration problem was used in the experiment to confirm the effectiveness of the proposed reinforcement learning system.