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