In recent years, unmanned aerial vehicle aerial photography has developed rapidly. Unmanned aerial vehicle can get a different perspective and allow us to do more difficult tasks. Controlling unmanned aerial vehicle requires a lot of manpower, so there are a number of studies that use reinforcement learning to make the unmanned aerial vehicle fly autonomously. It is an expensive and time-consuming task to use reinforcement learning and training unmanned aerial vehicle to accomplish specific tasks in a realistic environment. Therefore this study in a virtual environment using the Q - learning training unmanned aerial vehicle landing, then transplanted model of virtual environment in which to train good into real environment, makes the realistic environment of unmanned aerial vehicle can use cheaper and quickly achieve the same task.