  This paper investigates an unmanned aerial vehicle (UAV)-assisted wireless
powered mobile-edge computing (MEC) system, where the UAV powers the mobile
terminals by wireless power transfer (WPT) and provides computation service for
them. We aim to maximize the computation rate of terminals while ensuring
fairness among them. Considering the random trajectories of mobile terminals,
we propose a soft actor-critic (SAC)-based UAV trajectory planning and resource
allocation (SAC-TR) algorithm, which combines off-policy and maximum entropy
reinforcement learning to promote the convergence of the algorithm. We design
the reward as a heterogeneous function of computation rate, fairness, and
reaching of destination. Simulation results show that SAC-TR can quickly adapt
to varying network environments and outperform representative benchmarks in a
variety of situations.
