Reinforcement learning based on FNN and its application in robot navigation

Abstract

Behavior-based robot navigation is studied.The fuzzy neural network(FNN)and reinforcement learning(RL) are integrated.RL is utilized for structure identification and parameters tuning of FNN.The problem of continuous,infinite states and actions in RL is solved by using the function approximation of FNN.Furthermore,the residual algorithm is applied to the FNN learning,which guarantees the convergence and rapidity.Then,the learning results are employed to design the controller of the reactive robot system,by which the problem of navigation under complicated environment is solved effectively.