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In this paper, the design of an optimal trajectory for an energy-constraineddrone operating in dynamic network environments is studied. In the consideredmodel, a drone base station (DBS) is dispatched to provide uplink connectivityto ground users whose demand is dynamic and unpredictable. In this case, theDBS's trajectory must be adaptively adjusted to satisfy the dynamic user accessrequests. To this end, a meta-learning algorithm is proposed in order to adaptthe DBS's trajectory when it encounters novel environments, by tuning areinforcement learning (RL) solution. The meta-learning algorithm provides asolution that adapts the DBS in novel environments quickly based on limitedformer experiences. The meta-tuned RL is shown to yield a faster convergence tothe optimal coverage in unseen environments with a considerably low computationcomplexity, compared to the baseline policy gradient algorithm. Simulationresults show that, the proposed meta-learning solution yields a 25% improvementin the convergence speed, and about 10% improvement in the DBS' communicationperformance, compared to a baseline policy gradient algorithm. Meanwhile, theprobability that the DBS serves over 50% of user requests increases about 27%,compared to the baseline policy gradient algorithm.

Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks