用元强化学习优化无人机数据采集轨迹，兼顾信息新鲜度与能耗
Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks
- 结合DQN与元学习，让无人机快速适应不同优化目标
- 相比传统方法收敛更快，实现更低的时效性与功耗
- 适合低功耗物联网中动态环境下的智能调度
信息时效性（AoI）和传输功耗是低功耗无线网络中的关键性能指标，尤其在信息新鲜度至关重要的场景下。本文研究由飞行无人机支持的功耗受限物联网网络，旨在通过优化无人机飞行轨迹与调度策略，最小化随时间变化的AoI与传输功耗组合。为此，提出一种融合深度Q网络（DQN）与模型无关元学习（MAML）的元深度强化学习方法：DQN用于决策优化，MAML使算法能高效适应新目标函数。数值结果表明，所提算法收敛速度更快，对新目标的适应能力更强，整体实现了最低的AoI与传输功耗。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。