  In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial
vehicle (UAV) communication system is studied, where the UAV is deployed to
serve the user equipment (UE) with the assistance of multiple IRSs mounted on
several buildings to enhance the communication quality between UAV and UE. We
aim to maximize the energy efficiency of the system, including the data rate of
UE and the energy consumption of UAV via jointly optimizing the UAV's
trajectory and the phase shifts of reflecting elements of IRS, when the UE
moves and the selection of IRSs is considered for the energy saving purpose.
Since the system is complex and the environment is dynamic, it is challenging
to derive low-complexity algorithms by using conventional optimization methods.
To address this issue, we first propose a deep Q-network (DQN)-based algorithm
by discretizing the trajectory, which has the advantage of training time.
Furthermore, we propose a deep deterministic policy gradient (DDPG)-based
algorithm to tackle the case with continuous trajectory for achieving better
performance. The experimental results show that the proposed algorithms achieve
considerable performance compared to other traditional solutions.
