图表MADDPG与RNN用于多代理合作环境的RNN
Xiaolong Wei1,2, WenPeng Cui1, Xianglin Huang2
1Department of Energy Efficiency, Beijing SmartChip Microelectronics Technology Co., Ltd., Beijing, China.
Frontiers in neurorobotics
|July 17, 2023
概括
本研究介绍了一种使用图形注意力网络的新型合作型多代理模型. 该模型在复杂环境中增强了可扩展性和稳定性,优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 计算机科学 计算机科学
背景情况:
- 多代理系统在不确定的环境中面临着重大可扩展性挑战.
- 现有的模型很难有效地管理复杂的代理互动和连续行动空间.
研究的目的:
- 通过使用图形注意力网络,提出一种新的多代理合作模式.
- 解决多代理系统中的可扩展性和稳定性问题.
- 有效地建模代理关系和连续行动空间.
主要方法:
- 利用图表注意力网络,用于代理关系的图形卷积.
- 使用循环神经网络来定义连续行动空间.
- 使用图形神经网络和循环神经网络编码的交互权重.
主要成果:
- 拟议的模型在可扩展性和稳定性方面表现出卓越的性能.
- 在3D战争游戏引擎中的实验模拟验证了模型的有效性.
- 该模型显示,与最先进的方法相比,学习效率有所提高.
结论:
- 基于图表注意力网络的多代理模型提供了显著的进步.
- 该方法有效地处理环境不确定性和复杂的代理相互作用.
- 该模型为多代理系统提供了强大且可扩展的解决方案.
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