A Hierarchical Learning System Incorporating with Supervised, Unsupervised and Reinforcement Learning

Authors:
Jinglu Hu;Takafumi Sasakawa;Kotaro Hirasawa;Huiru Zheng
Affiliations:
Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;Waseda University, Kitakyushu, Fukuoka, Japan;University of Ulster, N.Ireland, UK
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 3
Cited 0

What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?

Neural Networks - Special issue on organisation of computation in brain-like systems
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Self-Organizing Maps

Self-Organizing Maps

Quantified Score

Hi-index	0.00

Visualization

Abstract

According to Hebb's Cell assembly theory, the brain has the capability of function localization. On the other hand, it is suggested that the brain has three different learning paradigms: supervised, unsupervised and reinforcement learning. Inspired by the above knowledge of brain, we present a hierarchical learning system consisting of three parts: supervised learning (SL) part, unsupervised learning (UL) part and reinforcement learning (RL) part. The SL part is a main part learning input-output mapping; the UL part realizes the function localization of learning system by controlling firing strength of neurons in SL part based on input patterns; the RL part optimizes system performance by adjusting parameters in UL part. Simulation results confirm the effectiveness of the proposed hierarchical learning system.