.png){kind=icon}
返回
Multiagent trajectory decision-making for complex systems based on transformer large model structure
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.7648.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
complex decision-making systems
decision-making optimization
multiagent nodes
trajectory planning
transformer large model
期刊
IF:
3.2
论文数:
7.0K
被引数:
1.4W
机构
暂无机构信息
引用论文
Transformer-Based Imitative Reinforcement Learning for Multirobot Path Planning基于Transformer的多机器人路径规划模拟强化学习
PREAI
分享收藏Mining the electrification potential of fuel-based vehicles mobility patterns: a data-based approach
PREAI
分享收藏
RoMAT: Role-based multi-agent transformer for generalizable heterogeneous cooperation.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏分享收藏
查看更多内容