Abstract
Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations. First, they typically rely on an exponential decay property of agent interactions on far-away nodes, which can be exploited to develop more efficient and tractable MARL algorithms. When this exponential decay property does not hold, these algorithms do not account for long-range interactions such as epidemic outbreaks or cascading power failures. Second, existing approaches lack network generalizability, or the ability to generalize to networks of different topological structure and scale than those seen during training. In this work, we first present a mean-field stability analysis and empirical study investigating the conditions for long-range network interactions. These results motivate our primary contribution: STACCA (Shared Transformer Actor-Critic with Counterfactual Advantage), a transformer-based MARL framework that addresses both long-range interactions and network generalizability. STACCA employs a centralized Graph Transformer Critic to model long-range dependencies and provide system-level feedback, while its shared Graph Transformer Actor learns a generalizable policy capable of adapting across diverse network topologies. To improve credit assignment during training, STACCA integrates a novel counterfactual advantage estimator that is compatible with state-value critic estimates. We evaluate STACCA on epidemic containment and rumor-spreading network control tasks, demonstrating improved performance and network generalizability. These results highlight the potential of transformer-based MARL architectures to achieve generalizable control in large-scale networked systems.
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May 25, 2026cs.MA
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