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Subsampling agent states in cooperative MARL provably converges to approximate Nash equilibria, enabling efficient learning in communication-constrained multi-agent systems.
Achieve near-optimal multi-agent coordination with heterogeneous agents while drastically reducing computational cost by intelligently subsampling interactions.
