EIA-SEC: Improved Actor-Critic Framework for Multi-UAV Collaborative Control in Smart Agriculture

TL;DR

This paper introduces EIA-SEC, a novel actor-critic framework for multi-UAV control in smart agriculture, enhancing collaboration, learning efficiency, and stability in UAV trajectory planning.

Contribution

The paper presents a new EIA-SEC framework that improves multi-UAV cooperative control by integrating elite imitation and shared ensemble critics, advancing beyond existing methods.

Findings

EIA-SEC outperforms baseline methods in reward performance.

EIA-SEC achieves higher training stability.

EIA-SEC converges faster in UAV trajectory tasks.

Abstract

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.