In this paper,an application of reinforcement learning to a radio-controlled helicopter is considered.An Actor-Critic algorithm is employed as reinforcement learning.A larening agent is composed of Actor and Criticthat are realized by using RBF neural networks.In the 3-dimensional model,a manipulation of helicopter needs 4 control signals for each of which one learning agent is assignd.Through computer simulation,it is observed that the agents learned flight controls to keep constant target altitude stably,however it couldn't learn flight controls of hovering.
