[2606.07557] SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
[Submitted on 25 May 2026]
Title:SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
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Abstract: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 $O(m \cdot n \cdot \chi^2)$ 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.
Comments: 11 pages, 2 figures, 1 tables, 6 sections
Subjects:
Machine Learning (cs.LG); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
Cite as: arXiv:2606.07557 [cs.LG]
(or arXiv:2606.07557v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07557
arXiv-issued DOI via DataCite
Submission history
From: Zhaowen Fan [view email] [v1] Mon, 25 May 2026 07:45:51 UTC (106 KB)
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