多代理层次图 注意力 演员-批判性 强化 学习
Tongyue Li1, Dianxi Shi1, Songchang Jin1
1Academy of Military Sciences, Beijing 100097, China.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
我们介绍了一个层次图注意力演员-关键强化学习方法来改善多代理系统. 这种方法通过将代理互动模型作为图表来增强可扩展性和适应性,从而解决复杂的通信需求.
科学领域:
- 人工智能的人工智能
- 强化学习是一种强化学习.
- 多代理系统 多代理系统
背景情况:
- 多代理系统面临的挑战是通信,相互作用的复杂性和可转移性.
- 可扩展性和复杂的信息交换阻碍了动态环境中的性能.
研究的目的:
- 提出一个新的等级图注意力演员-批判强化学习方法.
- 解决多代理系统中的可扩展性和复杂交互问题.
主要方法:
- 利用图形神经网络将代理观测编码成固定维度嵌入式.
- 采用分层图表注意力,并使用"间代理"和"间组"层用于上下文化状态表示.
- 整合了课程学习框架,以提高大规模任务的可转移性.
主要成果:
- 提出的方法有效地模拟了复杂的合作和竞争性代理关系.
- 在各种多代理任务中实现了更好的适应性,可扩展性和稳定性.
- 通过课程学习证明了大规模应用的增强可转移性.
结论:
- 层次图的注意力演员-关键方法为多代理强化学习提供了一个可扩展和可适应的解决方案.
- 这种方法有效地捕捉了在个人和团体层面上复杂的药物相互作用.
- 与课程学习的整合大大提高了大规模场景中的表现.
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