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.

Key words: Q learning, radial basis function neural network（RBFNN), robot autonomous navigation