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

Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint