Application of Reinforcement Learning Based on Radial Basis Function Neural Networks in Robot Navigation

Abstract

In a complex and continuous environment,Reinforcement Learning system will cause the dimensional disaster and generalization is often adopted to reduce the complexity of input space.Radial Basis Function Neural Networks(RBFNN:Radial Basis Function Neural Networks) has the function of strong approximation and generalization.Reinforcement Learning based on RBFNN is proposed,and it is used in the single-robot navigation.In the learning system,the state space and Q function are approximated by RBFNN.Simulation results show that the proposed method improves the ability of robot's collision avoidance so that the robot has better environment adaptability.