The Mean-Field approximation is a tractable approach for studying large
population dynamics. However, its assumption on homogeneity and universal
connections among all agents limits its applicability in many real-world
scenarios. Multi-Population Mean-Field Game (MP-MFG) models have been
introduced in the literature to address these limitations. When the underlying
Stochastic Block Model is known, we show that a Policy Mirror Ascent algorithm
finds the MP-MFG Nash Equilibrium. In more realistic scenarios where the block
model is unknown, we propose a re-sampling scheme from a graphon integrated
with the finite N-player MP-MFG model. We develop a novel learning framework
based on a Graphon Game with Re-Sampling (GGR-S) model, which captures the
complex network structures of agents' connections. We analyze GGR-S dynamics
and establish the convergence to dynamics of MP-MFG. Leveraging this result, we
propose an efficient sample-based N-player Reinforcement Learning algorithm for
GGR-S without population manipulation, and provide a rigorous convergence
analysis with finite sample guarantee.

多人群平均场博弈模型的研究中，利用均场近似可以找到纳什均衡，在实际应用中，由于普适假设的限制，我们提出了一种基于图纹重抽样的学习框架，用于捕捉智能体连接的复杂网络结构，并通过分析其动力学与多人群平均场博弈动力学之间的收敛关系，提出了一种高效的基于样本的多智能体强化学习算法，而无需进行群体操作，并对其收敛性进行了严格的有限样本保证分析。