SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
Researchers introduce SPIN, a framework that models multi-agent communication topologies as compressed tensor networks to address the combinatorial scaling problem in decentralized swarm control. By factorizing joint policy tensors into matrix product state chains, SPIN achieves linear scaling in clique length and removes the need for online training or scenario-specific optimization. Simulation experiments show that SPIN functions as a reusable decentralized coordination layer, with notable gains in multi-goal coordination and dense local interaction regimes.
Why it matters: SPIN could enable scalable, reusable swarm control for applications like drone swarms and multi-robot systems without retraining or scenario-specific optimization.
Full story at: arXiv Multiagent Systems ↗