###### Abstract  
For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones--acting as agents--can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 10-45% higher than that of the other three methods, and it can save up to 3% more energy. drones; multi-agent deep reinforcement learning (MADRL); energy optimization; emergency communications +
Footnote †: journal: _Future Internet_  
the efficient deployment and operation of UAVs in disaster scenarios present significant challenges. The dynamic nature of the environment, varying user densities, and the limited battery life of drones necessitate a strategic approach to their deployment. Optimizing the flight paths of drones to maximize user coverage while minimizing energy consumption is a complex problem that requires advanced computational techniques [6]. This paper proposes a novel solution to this problem by employing deep reinforcement learning (DRL) algorithms to control the movement trajectories of drones. DRL, a subset of machine learning, is well-suited for problems involving sequential decision-making under uncertainty [7]. By training drones to learn the optimal strategy, our approach ensures that they can adapt to real-time changes in the environment and user equipment (UE) distribution, taking into account the energy consumption of the drones. Based on this, it can be said that drones have the capability of providing efficient and reliable communication services. The primary contributions of this research are described below. * To address drone-assisted emergency communications in disaster scenarios, we first modeled the movement of rescue workers in post-disaster situations. Then, based on this model, we developed a DRL-based algorithm specifically for the service of drones in disaster scenarios. * During the communication between drones and UEs, we used a novel signal to interference and noise ratio (SINR) calculation method, taking into account the communication interference generated between the drones. We set and analyzed the communication threshold to ensure QoS and used this to calculate the coverage of drones to UEs. * We carefully designed the reward function and considered both coverage and energy consumption terms to ensure that the system provides motivating reward values. * We conducted extensive simulations to evaluate the performance of our approach, demonstrating significant improvements in user coverage and energy efficiency compared to the conventional methods. The remainder of this paper is organized in the following manner: Section 2 reviews the related work in the field of UAV-based communication and reinforcement learning applications.