Collaborative Transmission and Computation for Distributed AGV Systems: A Transformer-Based MADRL Approach

Highly flexible automated guided vehicles (AGVs) are interconnected via industrial wireless control networks (IWCNs) in multiaccess edge computing (MEC)-assisted smart factories. The MEC alleviates the lack of computational resources in AGV systems through task offloading. However, IWCNs with limited communication resources struggle to support the highly concurrent offloading of AGVs. In the distributed AGV systems with multi-MEC servers, AGV mobility leads to uneven distribution across MEC server areas, potentially resulting in severe competition for communication resources. Therefore, in this article, we design a Transferable joint Task Offloading and multichannel access (T2OMCA) algorithm based on multiagent deep reinforcement learning. Specifically, AGV observations are modeled as graphs, in which edge relationships are learned through Transformer. This enables AGVs to utilize domain information to collaborate and alleviate concurrent offloading. Moreover, the T2OMCA algorithm converts network input into fixed embeddings to accommodate varying numbers of AGVs. Finally, to encourage exploration in the high-dimensional action space, the T2OMCA algorithm introduces a noisy network and a prioritized experience replay mechanism. Extensive simulations show that the T2OMCA algorithm outperforms existing algorithms in terms of average completion rate, processing delay, and access conflict rate under time-varying AGV topologies.