In many massive IoT communication scenarios, the IoT devices require coverage
from dynamic units that can move close to the IoT devices and reduce the uplink
energy consumption. A robust solution is to deploy a large number of UAVs (UAV
swarm) to provide coverage and a better line of sight (LoS) for the IoT
network. However, the study of these massive IoT scenarios with a massive
number of serving units leads to high dimensional problems with high
complexity. In this paper, we apply multi-agent deep reinforcement learning to
address the high-dimensional problem that results from deploying a swarm of
UAVs to collect fresh information from IoT devices. The target is to minimize
the overall age of information in the IoT network. The results reveal that both
cooperative and partially cooperative multi-agent deep reinforcement learning
approaches are able to outperform the high-complexity centralized deep
reinforcement learning approach, which stands helpless in large-scale networks.

应用多智能体深度强化学习来解决大型物联网场景中的高维问题，通过部署一群无人机 (UAV swarm) 为物联网网络提供覆盖，并最小化信息时效，结果表明合作和部分合作的多智能体深度强化学习方法能够优于复杂度高的集中式深度强化学习方法。