Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.

翻译：信息年龄（AoI）与传输功耗是低能耗无线网络中的关键性能指标，其中信息新鲜度至关重要。本研究探讨了一个由飞行无人机（UAV）支持进行数据采集的功率受限物联网（IoT）网络。我们的目标是优化无人机的飞行轨迹与调度策略，以最小化时变的AoI与传输功耗组合。为应对此变化，本文提出一种元深度强化学习（RL）方法，将深度Q网络（DQN）与模型无关元学习（MAML）相结合。DQN用于确定无人机的最优决策，而MAML则使算法能够适应不同的目标函数。数值结果表明，所提算法相较于传统深度RL方法收敛更快，对新目标的适应能力更强，整体实现了最小的AoI与传输功耗。