  In this paper, the design of an optimal trajectory for an energy-constrained
drone operating in dynamic network environments is studied. In the considered
model, a drone base station (DBS) is dispatched to provide uplink connectivity
to ground users whose demand is dynamic and unpredictable. In this case, the
DBS's trajectory must be adaptively adjusted to satisfy the dynamic user access
requests. To this end, a meta-learning algorithm is proposed in order to adapt
the DBS's trajectory when it encounters novel environments, by tuning a
reinforcement learning (RL) solution. The meta-learning algorithm provides a
solution that adapts the DBS in novel environments quickly based on limited
former experiences. The meta-tuned RL is shown to yield a faster convergence to
the optimal coverage in unseen environments with a considerably low computation
complexity, compared to the baseline policy gradient algorithm. Simulation
results show that, the proposed meta-learning solution yields a 25% improvement
in the convergence speed, and about 10% improvement in the DBS' communication
performance, compared to a baseline policy gradient algorithm. Meanwhile, the
probability that the DBS serves over 50% of user requests increases about 27%,
compared to the baseline policy gradient algorithm.
