Unmanned Aerial Vehicles (UAVs) have become integral to numerous applications, prompting research towards enhancing their capabilities. For time-critical missions, minimizing latency is crucial; however, current studies often rely on sending data to ground station or cloud for processing due to their limited onboard capacities. To leverage the networking capabilities of UAVs, recent research focuses on enabling data processing and offloading within the UAV network for coordinated decision-making. This paper explores a multi-hop data offloading scheme designed to optimize the task processing and resource management of UAVs. The proposed distributed strategy uses multi-agent reinforcement learning, where UAVs, each with varying computational capacities and energy levels, process and offload tasks while managing energy consumption and latency. The agents, represented as actor-critic models, learn and adapt their actions based on current state and environment feedback. The study considers a consensus-based method to update learning weights, promoting cooperative behavior among the agents with minimum interaction. Through multiple training episodes, the agents improve their performance, with the overall system achieving faster convergence with high rewards, demonstrating the viability of decentralized data processing and offloading in UAV networks.

中文翻译：

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使用 MARL 的无人机网络中的分布式多跳数据处理


无人机 （UAV） 已成为众多应用不可或缺的一部分，促使研究提高其功能。对于时间关键型任务，最大限度地减少延迟至关重要;然而，由于机载容量有限，目前的研究通常依赖于将数据发送到地面站或云进行处理。为了利用无人机的网络功能，最近的研究重点是在无人机网络内实现数据处理和卸载，以实现协调决策。本文探讨了一种多跳数据卸载方案，旨在优化无人机的任务处理和资源管理。拟议的分布式策略使用多智能体强化学习，其中无人机具有不同的计算能力和能量水平，在管理能耗和延迟的同时处理和卸载任务。代理以演员-评论家模型表示，根据当前状态和环境反馈学习和调整他们的行动。该研究考虑了一种基于共识的方法来更新学习权重，以最少的交互促进代理之间的合作行为。通过多次训练，智能体提高了他们的性能，整个系统实现了更快的收敛和高回报，展示了去中心化数据处理和卸载在无人机网络中的有效性。