This paper presents a machine-learning algorithm applied to a quadcopter application. We are proposing a fuzzy actor-critic learning (FACL) algorithm. This method enables a pursuer quadcopter to capture an evader quadcopter in the pursuit-evasion (PE) differential game. In this application, the pursuer learns its control strategies by interacting with evader and learning from past experiences. Both the critique and the actor are fuzzy inference systems (FIS). It is assumed that the pursuer knows only the instantaneous position and speed of the evader and vice versa. The FACL will generate the desired trajectory as the input for low-level controllers. Simulation results are presented for the PE differential game to demonstrate the practicality of our machine-learning algorithm.