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Learning Multi-Agent Coordination via Sheaf-ADMM

Sheaf-ADMM combines sheaf theory and ADMM to enable decentralized multi-agent consensus. Agents solve local subproblems and negotiate with neighbors, achieving global coordination without a central orchestrator. Experiments on Sudoku, maze pathfinding, and image classification show its effectiveness and interpretability.

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Learning Multi-Agent Coordination via Sheaf-ADMM
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Key points

  • Sheaf-ADMM uses sheaf theory to define linear projection agreement between agents, integrated with ADMM for local optimization and global negotiation.
  • Each agent processes only a local view; iterative local solves and sheaf diffusion lead to global consensus.
  • On multi-agent Sudoku, it achieves 92.6% solve rate compared to 34.7% for MPNN baselines.
  • The linear communication structure makes the system highly interpretable and amenable to analysis.

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