量子化分布式在线盗优化在零和游戏中的动态遗憾
IEEE transactions on cybernetics
|September 16, 2025
概括
本研究介绍了在两个竞争网络中进行分布式在线优化的新算法,解决了量子化通信和有限反等挑战. 开发的方法确保了网络回报,并实现了线下遗憾界限.
科学领域:
- 分布式优化 分布式优化
- 游戏理论 游戏理论
- 多代理系统 多代理系统
- 网络科学 网络科学
背景情况:
- 在零和游戏环境中研究分布式在线优化.
- 专注于两个不同的,时间变化的多代理网络.
- 解决网络间通信和信息收集的挑战.
研究的目的:
- 在两个网络的零和游戏中设计和分析分布式在线优化算法.
- 在复杂的通信约束下,保证每个网络的回报.
- 为了测量使用动态纳什平衡的性能.
主要方法:
- 介绍了双网络 (QDOBO-TN) 算法中的量化分布式在线盗优化.
- 集成的量子化通信和强盗反机制.
- 代理人传输量化信息,并使用带有部分成本函数反的一点估计器.
主要成果:
- 开发了QDOBO-TN和一个多时代变体,以确保网络回报.
- 实现了两种算法相对于代数T的亚线性遗憾边界.
- 通过广泛的模拟实验验验证了算法的有效性.
结论:
- 拟议的QDOBO-TN算法有效地解决了复杂的双网络零和游戏中的分布式在线优化.
- 该算法表现出强大的性能和次线性遗憾,适用于时间变化和量子化环境.
- 模拟结果证实了开发方法的实际适用性和效率.
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