通过时空信息聚合的基于多代理途径的注意力网络的图表
Qingling Zhang1, Peng Wang1, Cui Ni1
1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China.
PloS one
|June 16, 2025
概括
本研究引入了一种新的信息融合方法,用于多代理途径寻找 (MAPF),以克服部分可观测性. 该方法提高了代理路径规划的准确性和效率,特别是在复杂的环境中.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 多代理路径查找 (MAPF) 算法面临部分可观测性的挑战,阻碍了最佳策略的确定.
- 有效的信息融合对于扩大代理观察范围和提高MAPF系统性能至关重要.
研究的目的:
- 为MAPF开发一个强大的信息融合方法,通过整合时间和空间维度来解决部分可观测性.
- 在复杂,动态环境中提高MAPF算法的精度和时间效率.
主要方法:
- 一种新的融合方法,利用图表注意网络 (GAT) 进行代理间通信和缩放的点-产品聚合.
- 整合门式循环单元 (GRU) 和卷积神经网络 (CNN) 进行历史观测编码.
- 将聚合的代理信息解码为最终的行动策略,以解决部分可观测性问题.
主要成果:
- 拟议的方法显著提高了准确性和时间效率,超过现有的GNN和GAT方法高达73%.
- 在较大的地图中,性能增长更为显著,证明了算法的可扩展性.
- 与GNN和GAT.相比,实现了24.5%,47%,37.5%和73%的精度和时间效率改进.
结论:
- 开发的信息融合技术有效地减轻了MAPF中的部分可观测性.
- 基于GAT的方法为复杂的多代理导航任务提供了可扩展和高效的解决方案.
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