Optimizing Age of Information in Industrial IOT: A Muilti-Agent Deep Reinforcement Learning Approach Based on Meta-Learning i

Age of Information (AoI) is an important metric for measuring data freshness and is crucial for applications such as video transmission, IoT, and communication networks. Optimizing AoI is particularly critical in Industrial IoT, where advanced communication technologies are applied to traditional manufacturing industries, leading to scenarios that demand higher responsiveness and agility from industrial systems. Deep Reinforcement Learning (DRL), which combines the perception capability of deep learning with the decision-making capability of reinforcement learning, has been widely applied in various fields but faces many challenges in dynamic environments, such as difficult parameter adjustment and long training times. To improve DRL's adaptation speed to new tasks, we propose a multi-agent optimization method that combines PPO (Proximal Policy Optimization) and MAML (Model-Agnostic Meta-Learning) algorithms. By training a general strategy through meta-learning in different task scenarios, the method can quickly adapt to new tasks. Experimental results show that this method performs excellently in optimizing the age of information in video transmission.