提出EIA-SEC框架，提升多无人机农业协同控制的效率与稳定性
EIA-SEC: Improved Actor-Critic Framework for Multi-UAV Collaborative Control in Smart Agriculture
- 采用精英模仿+共享集成评论家机制，减少试错成本
- 在奖励、训练稳定性和收敛速度上优于现有方法
- 适合智能农业中多无人机协同任务场景
无线通信技术的广泛应用推动了智慧农业的发展，无人飞行器（UAV）在此中承担数据采集、图像获取和通信等多重任务。针对多UAV智慧农业系统，本文构建马尔可夫决策过程以解决多无人机轨迹规划问题。提出一种新型精英模仿-共享集成评论家（EIA-SEC）框架：代理通过自适应学习精英代理降低试错成本，共享集成评论家与各代理本地评论家协作，确保目标值估计无偏并防止过估计。实验结果表明，EIA-SEC在奖励性能、训练稳定性和收敛速度方面均优于当前先进基线方法。
原文摘要 · Abstract (English)
The widespread application of wireless communication technology has promoted the development of smart agriculture, where unmanned aerial vehicles (UAVs) play a multifunctional role. We target a multi-UAV smart agriculture system where UAVs cooperatively perform data collection, image acquisition, and communication tasks. In this context, we model a Markov decision process to solve the multi-UAV trajectory planning problem. Moreover, we propose a novel Elite Imitation Actor-Shared Ensemble Critic (EIA-SEC) framework, where agents adaptively learn from the elite agent to reduce trial-and-error costs, and a shared ensemble critic collaborates with each agent's local critic to ensure unbiased objective value estimates and prevent overestimation. Experimental results demonstrate that EIA-SEC outperforms state-of-the-art baselines in terms of reward performance, training stability, and convergence speed.
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