Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning

Abstract

Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales exponentially with the number of agents. Mean-field methods alleviate this burden by aggregating agent interactions, but these approaches assume homogeneous interactions. Recent graphon-based frameworks capture heterogeneity, but are computationally expensive as the number of agents grows. Therefore, we introduce $\text{GMFS}$, a $\text{G}$raphon $\text{M}$ean-$\text{F}$ield $\text{S}$ubsampling framework for scalable cooperative MARL with heterogeneous agent interactions. By subsampling $\kappa$ agents according to interaction strength, we approximate the graphon-weighted mean-field and learn a policy with sample complexity $\mathrm{poly}(\kappa )$ and optimality gap $O(1/\sqrt{\kappa })$. We verify our theory with numerical simulations in robotic coordination, showing that $\text{GMFS}$ achieves near-optimal performance.