使用集群同步激光网络的分散的多代理强化学习算法
Shun Kotoku1, Takatomo Mihana1, André Röhm1
1The University of Tokyo, Department of Information Physics and Computing, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Physical review. E
|February 7, 2025
概括
本研究介绍了一种用于多代理强化学习 (MARL) 的新型光子算法,以解决竞争性多武装强盗 (CMAB) 问题,使得在没有直接信息共享的情况下进行合作决策.
科学领域:
- 物理 物理学 物理
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 多代理强化学习 (MARL) 对无线网络和自动驾驶等领域至关重要.
- 在MARL中,具有竞争力的多武器强盗 (CMAB) 问题是一个基本的挑战.
研究的目的:
- 为CMAB问题提出基于光子的决策算法.
- 在MARL中使用物理过程来展示分散的合作决策.
主要方法:
- 使用光学合的激光器,表现出混乱的振荡和集群同步.
- 实现一个分散的合调整算法.
- 进行数值模拟以验证方法.
主要成果:
- 光子算法有效地平衡了CMAB中的勘探和开发.
- 合作决策在没有代理人之间明确的信息共享的情况下实现.
- 由简单的算法控制的复杂物理过程使分散的强化学习成为可能.
结论:
- 可利用光子系统进行先进的MARL解决方案.
- 在MARL中,通过物理系统动态实现了分散的控制.
- 拟议的方法为合作性AI提供了一种新的方法.
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