Dynamic Collaborative Mechanism of Multi-Agent Supply Chain Driven by AI: Based on IoT and RL

Abstract

This article proposes a multi-agent dynamic collaboration mechanism based on the Internet of Things (IoT) and reinforcement learning (RL) to address the problems of low efficiency and insufficient dynamic response in multi-agent collaboration in intelligent supply chains. Designed a dynamic collaborative mechanism architecture for supply chain driven by AI. By integrating the real-time data collection capability of the Internet of Things with the autonomous decision-making optimization capability of reinforcement learning, a distributed multi-agent collaborative framework is constructed to achieve dynamic resource allocation and adaptive optimization throughout the entire supply chain. This article combines theoretical modeling and empirical analysis to verify the effectiveness of the mechanism in scenarios such as inventory management, logistics scheduling, and risk warning, providing theoretical support and technical paths for the digital transformation of intelligent supply chains.