Multiagent trajectory decision‐making for complex systems based on transformer large model structure

Abstract The article explores the application of transformer‐based large models for trajectory decision‐making in complex decision systems, focusing on multiagent nodes in a topological structure. By leveraging the capabilities of large models, the proposed approach enhances decision‐making in model‐free control environments characterized by numerous agents, diverse internal tasks, and unknown conditions. The proposed framework integrates advanced transformer models with a sophisticated decision‐making framework to enhance the efficiency, safety, and reliability of multiagent operations in dynamic environments. The results demonstrate the superior performance of the transformer model, achieving a trajectory decision‐making accuracy of 97.8%, significantly outperforming other models such as long short‐term memory (LSTM), gate recurrent unit (GRU), and traditional approaches. The visualizations highlight the close alignment between the true and predicted outcomes of complex decision‐making systems, showcasing the model's ability to capture complex dependencies and interactions within the multiagent environment. High task completion rates and low collision rates further underscore the effectiveness of the decision‐making framework in optimizing agent coordination and ensuring safe operations.