Model Predictive Control

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Model Predictive Control

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Lyapunov Function

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SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
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May 18, 2026Branden Frieden, James M. Ferguson, Alan Kuntz +1Soft Robots Neural Operators
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Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
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May 25, 2026 · cs.LGJ/K move · Enter open · S save

SPIN: Decentralized Swarm Control via Tensorized Policy Coordination

Zhaowen Fan

Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joint action spaces and high-latency communication overhead. This paper introduces the Swarm Policy Interference Network (SPIN) framework, an architectural paradigm that bypasses these limitations by modeling swarm topologies as a compressed tensor network. We factorize the joint policy tensors of local multi-agent cliques into Matrix Product State (MPS) chains, reducing the computational complexity of evaluation from an exponential

O(n^m)

wall to a strictly linear

O(m \cdot n \cdot χ^2)

constraint. To bridge local continuous spatial geometry with this discrete algebraic backend without requiring power-intensive online training loops, we introduce a decoupled, hybrid neuro-symbolic control pipeline. Local multi-layered neural networks operate as structural coordination encoders, pre-trained offline to nonlinearly map hand-engineered geometric descriptors into abstract environmental target measures. At runtime, edge agents execute instantaneous behavioral adaptations by applying the Radon-Nikodým derivative directly as a zero-shot importance-reweighting filter. We validate the framework within a discrete-time multi-agent simulation sandbox spanning tracking, decentralized dispersion/area coverage, and multi-goal coordination regimes. Qualitative telemetry demonstrates that the integrated pipeline achieves stable target-directed motion, anti-collapse spatial spreading under decentralized constraints, and structured subgroup formation across multiple targets, providing a mathematically grounded route to tractable, low-power edge swarm intelligence.

Swarms Neural Control Strategic Agents

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