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[2602.16196] Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
Summary
This paper introduces Graphon Mean-Field Subsampling (GMFS), a framework for scalable cooperative multi-agent reinforcement learning (MARL) that addresses the challenge of heterogeneous agent interactions while maintaining computational efficiency.
Why It Matters
The research addresses a significant challenge in multi-agent reinforcement learning by providing a method that accommodates heterogeneous interactions among agents. This is crucial for real-world applications where agents may have varying capabilities and roles, enhancing the scalability and effectiveness of MARL systems.
Key Takeaways
- GMFS enables efficient coordination of large populations of heterogeneous agents in MARL.
- The framework reduces computational complexity by subsampling agents based on interaction strength.
- Theoretical performance guarantees include a sample complexity of poly(κ) and an optimality gap of O(1/√κ).
- Numerical simulations demonstrate GMFS achieving near-optimal performance in robotic coordination tasks.
- The approach enhances the applicability of MARL in complex, real-world scenarios.
Computer Science > Machine Learning arXiv:2602.16196 (cs) [Submitted on 18 Feb 2026] Title:Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning Authors:Emile Anand, Richard Hoffmann, Sarah Liaw, Adam Wierman View a PDF of the paper titled Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning, by Emile Anand and 3 other authors View PDF HTML (experimental) 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 $\texttt{GMFS}$, a $\textbf{G}$raphon $\textbf{M}$ean-$\textbf{F}$ield $\textbf{S}$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 $\texttt{GMFS}$ achieves near-optimal performance. Comments: Subjects: Mach...
