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In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) asflying base stations (BSs) for optimizing the throughput of mobile users isinvestigated for UAV networks. This problem is formulated as a time-varyingmixed-integer non-convex programming (MINP) problem, which is challenging tofind an optimal solution in a short time with conventional optimizationtechniques. Hence, we propose an actor-critic-based (AC-based) deepreinforcement learning (DRL) method to find near-optimal UAV positions at everymoment. In the proposed method, the process searching for the solutioniteratively at a particular moment is modeled as a Markov decision process(MDP). To handle infinite state and action spaces and improve the robustness ofthe decision process, two powerful neural networks (NNs) are configured toevaluate the UAV position adjustments and make decisions, respectively.Compared with the heuristic algorithm, sequential least-squares programming andfixed UAVs methods, simulation results have shown that the proposed methodoutperforms these three benchmarks in terms of the throughput at every momentin UAV networks.

An Actor-Critic-Based UAV-BSs Deployment Method for Dynamic Environments