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A novel framework is proposed for the trajectory design of multiple unmanned aerial vehicles (UAVs) based on the prediction of users' mobility information. The problem ofjoint trajectory design and power control is formulated for maximizing the instantaneous sum transmit rate while satisfying the rate requirement of users. In an effort to solve this pertinent problem, a threestep approach is proposed, which is based on machine learning techniques to obtain both the position information of users and the trajectory design of UAVs. First, a multi-agent Q-learning-based placement algorithm is proposed for determining the optimal positions of the UAVs based on the initial location of the users. Second, in an effort to determine the mobility information of users based on a real dataset, their position data is collected from Twitter to describe the anonymous user-trajectories in the physical world. In the meantime, an echo state …