提出自适应竞争网络中的扩散学习算法，解决团队间博弈问题。
Diffusion Stochastic Learning Over Adaptive Competing Networks
- 设计分布式扩散算法，让团队内协作、跨团队动态博弈。
- 在弱交互零和与强交互非零和博弈中均实现稳定收敛。
- 适用于竞合经济建模和去中心化GAN训练等场景。
本文研究两个由协作代理组成的网络团队之间的随机动态博弈。与所有代理共享统一目标的完全合作情形不同，每个团队旨在最小化自身独立的目标。在对抗性设定下，其目标可能相互冲突，如零和博弈。竞赛过程中，代理在本团队内共享策略信息，同时推断并适应对方团队的策略。我们提出了扩散学习算法，以应对两类关键网络博弈：一是在弱跨团队子图交互下的零和博弈；二是具有强跨团队子图交互的一般非零和博弈。在合理假设下分析了所提算法的稳定性性能，并通过库诺特团队竞争和去中心化GAN训练实验验证理论结果。
原文摘要 · Abstract (English)
This paper studies a stochastic dynamic game between two competing teams, each consisting of a network of collaborating agents. Unlike fully cooperative settings, where all agents share a common objective, each team in this game aims to minimize its own distinct objective. In the adversarial setting, their objectives could be conflicting as in zero-sum games. Throughout the competition, agents share strategic information within their own team while simultaneously inferring and adapting to the strategies of the opposing team. We propose diffusion learning algorithms to address two important classes of this network game: i) a zero-sum game characterized by weak cross-team subgraph interactions, and ii) a general non-zero-sum game exhibiting strong cross-team subgraph interactions. We analyze the stability performance of the proposed algorithms under reasonable assumptions and illustrate the theoretical results through experiments on Cournot team competition and decentralized GAN training.
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