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This paper introduces SIGMA, a hierarchical framework for cooperative multi-agent reinforcement learning that addresses the challenges posed by structured noise effects in noisy observations. By organizing agents into adaptive local structures and employing intra-group consensus aggregation, SIGMA effectively preserves task-relevant information while smoothing representation deviations. Experimental results in StarCraft II show that SIGMA enhances robustness to observation noise without sacrificing performance in noise-free scenarios, highlighting the importance of exploiting cooperation structures in MARL.
SIGMA reveals that leveraging structured noise effects can significantly enhance robustness in multi-agent systems, outperforming traditional methods in noisy environments.
Cooperative multi-agent reinforcement learning (MARL) faces significant challenges in maintaining robust coordination under noisy observations. Although observation disturbances are often introduced independently across agents, their downstream effects on cooperative decision-making can become structured through underlying cooperation structures. We characterize this phenomenon as structured noise effects, where noise-induced decision effects exhibit local correlation among agents with stronger task-related dependencies while remaining globally heterogeneous across different agents and local structures. Existing robust MARL methods, however, rarely explicitly characterize or exploit such structure-dependent noise effects. To address this limitation, we propose SIGMA, a hierarchical collaboration framework that exploits cooperation structures to learn robust representations under noisy observations. SIGMA first organizes agents into adaptive local structures through density-based grouping and performs intra-group consensus aggregation to preserve shared task-relevant information while smoothing agent-specific representation deviations. Inter-group attention then adaptively integrates information across different groups to preserve global coordination while accommodating their heterogeneous contributions. Experiments on noisy-observation tasks in StarCraft II empirically validate the structured noise effects and demonstrate that SIGMA consistently improves robustness under observation noise while maintaining competitive performance in noise-free environments.
