A Multi‐Agent Soft Actor‐Critic Framework to Minimize Age of Information in UAV‐Assisted Networks

The integration of artificial intelligence (AI) and the Internet of Things (IoT) has given rise to AIoT systems. However, the accelerated proliferation of AIoT ecosystems introduces substantial communication and networking difficulties. Mobile edge computing (MEC) has emerged as a promising solution to address time‐sensitive computational demands. This paper addresses the improvement of information freshness, a critical enabler for real‐time decision‐making in dynamic, resource‐constrained AIoT environments. We formulate the UAV‐assisted MEC system as a Markov decision process with the objective of minimizing the age of information (AoI). To this end, we propose an adaptive federated multi‐agent soft actor‐critic framework for resource scheduling. This framework leverages maximum entropy to enable robust exploration and incorporates an innovative adaptive federated learning mechanism by adopting a trainable network to predict the parameter matrix of federated learning. This enables federated learning to better promote knowledge sharing among multiple agents, thereby accelerating convergence and improving performance. Experimental results validate that our approach significantly outperforms state‐of‐the‐art reinforcement learning based algorithms in AoI minimization, stability enhancement, and task completion volume improvement, thereby advancing the safeguarding of communication and networking in AIoT systems.