In this paper, the problem of the trajectory design for a group of
energy-constrained drones operating in dynamic wireless network environments is
studied. In the considered model, a team of drone base stations (DBSs) is
dispatched to cooperatively serve clusters of ground users that have dynamic
and unpredictable uplink access demands. In this scenario, the DBSs must
cooperatively navigate in the considered area to maximize coverage of the
dynamic requests of the ground users. This trajectory design problem is posed
as an optimization framework whose goal is to find optimal trajectories that
maximize the fraction of users served by all DBSs. To find an optimal solution
for this non-convex optimization problem under unpredictable environments, a
value decomposition based reinforcement learning (VDRL) solution coupled with a
meta-training mechanism is proposed. This algorithm allows the DBSs to
dynamically learn their trajectories while generalizing their learning to
unseen environments. Analytical results show that, the proposed VD-RL algorithm
is guaranteed to converge to a local optimal solution of the non-convex
optimization problem. Simulation results show that, even without meta-training,
the proposed VD-RL algorithm can achieve a 53.2% improvement of the service
coverage and a 30.6% improvement in terms of the convergence speed, compared to
baseline multi-agent algorithms. Meanwhile, the use of meta-learning improves
the convergence speed of the VD-RL algorithm by up to 53.8% when the DBSs must
deal with a previously unseen task.

本研究采用基于价值分解的强化学习算法来解决能量限制多个无人机在动态无线网络环境下的航迹规划问题，以最大化无人机服务的用户群组覆盖率，并通过元学习机制优化算法的性能。仿真结果显示，该算法相较于基准多智能体算法，能够分别提高 53.2% 的服务覆盖率和 30.6% 的收敛速度，并且在面对之前未见过的任务时，元学习可提高算法的收敛速度高达 53.8％。