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[2602.16966] A Unified Framework for Locality in Scalable MARL
Summary
This paper presents a unified framework addressing locality in scalable Multi-Agent Reinforcement Learning (MARL), proposing a novel policy-dependent approach to improve efficiency and performance.
Why It Matters
The research addresses significant challenges in MARL, particularly the curse of dimensionality. By introducing a policy-dependent locality framework, it provides a more nuanced understanding of how policies can influence learning dynamics, which is crucial for developing more effective multi-agent systems.
Key Takeaways
- Introduces a novel decomposition of the policy-induced interdependence matrix in MARL.
- Establishes that locality can be policy-dependent, enhancing learning efficiency.
- Derives a tighter spectral condition for exponential decay in value functions.
- Analyzes a localized block-coordinate policy improvement framework.
- Addresses the limitations of existing worst-case bounds in MARL.
Computer Science > Machine Learning arXiv:2602.16966 (cs) [Submitted on 19 Feb 2026] Title:A Unified Framework for Locality in Scalable MARL Authors:Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni, Lijun Chen View a PDF of the paper titled A Unified Framework for Locality in Scalable MARL, by Sourav Chakraborty and 3 other authors View PDF HTML (experimental) Abstract:Scalable Multi-Agent Reinforcement Learning (MARL) is fundamentally challenged by the curse of dimensionality. A common solution is to exploit locality, which hinges on an Exponential Decay Property (EDP) of the value function. However, existing conditions that guarantee the EDP are often conservative, as they are based on worst-case, environment-only bounds (e.g., supremums over actions) and fail to capture the regularizing effect of the policy itself. In this work, we establish that locality can also be a \emph{policy-dependent} phenomenon. Our central contribution is a novel decomposition of the policy-induced interdependence matrix, $H^\pi$, which decouples the environment's sensitivity to state ($E^{\mathrm{s}}$) and action ($E^{\mathrm{a}}$) from the policy's sensitivity to state ($\Pi(\pi)$). This decomposition reveals that locality can be induced by a smooth policy (small $\Pi(\pi)$) even when the environment is strongly action-coupled, exposing a fundamental locality-optimality tradeoff. We use this framework to derive a general spectral condition $\rho(E^{\mathrm{s}}+E^{\mathrm{a}}\Pi(\pi)) <...
