Foremost-Policy Reinforcement Learning Based ART2 Neural Network

Abstract

A foremost-policy reinforcement learning based ART2 neural network(FPRL-ART2)and its learning algorithm are proposed in this paper.To fit the requirement of real time learning,the first awarded behavior based on present states is selected in our Foremost-Policy Reinforcement Learning(FPRL)in stead of the optimal behavior in 1-step Q-Learning.The algorithm of FPRL is given and it is integrated with ART2 neural network.The stored weights of classified pattern in ART2 is increased or decreased by reinforcement learning.The FPRL-ART2 is successfully used in collision avoidance of mobile robot and the simulation experiment indicates that the times of collision between robot and obstacle is effectively decreased.The FPRL-ART2 makes favorable result of collision avoidance.