Age of information minimization in UAV-assisted data harvesting networks by multi-agent deep reinforcement curriculum learning

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly being used for data harvesting from Wireless Sensor Nodes (SNs). This study aims to minimize the Age of Information (AoI) during data collection, while also considering the energy sustainability of the UAVs. Addressing the challenge of optimizing performance in Multi-Agent Reinforcement Learning (MARL) systems as the number of agents increases, our study introduces a MARL approach combined with curriculum learning and evolutionary strategy. This innovation specifically targets the performance issue in traditional MARL setups when scaling up the number of agents. By applying curriculum learning and evolutionary strategies, our method not only enhances MARL scalability but also integrates energy-efficient charging mechanisms, effectively enhancing system performance in large-scale deployments. Numerical results show that our proposed algorithm outperforms baselines in terms of AoI and charging, proving its effectiveness in managing the complexities of large-scale MARL systems.