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Optimal Transport-Based Decentralized Multiagent Distribution Matching
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AI
DOI：10.1109/tac.2026.3668445.png)
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Abstract
En 中文
This article presents a decentralized control framework for distribution matching in multiagent systems, where agents collectively achieve a prescribed terminal spatial distribution. The problem is formulated using optimal transport (Wasserstein distance), which provides a principled measure of distributional discrepancy and serves as the basis for the control design. To avoid solving the global optimal transport problem directly, the distribution-matching objective is reformulated into a tractable per-agent decision process, enabling each agent to identify its desired terminal locations using only locally available information. A sequential weight-update rule is introduced to construct feasible local transport plans, and a memory-based correction mechanism is incorporated to maintain reliable operation under intermittent and range-limited communication. Convergence guarantees are established, showing cyclewise improvement of a surrogate transport cost under both linear and nonlinear agent dynamics. Simulation results demonstrate that the proposed framework achieves effective and scalable distribution matching while operating in a fully decentralized manner.
Keywords:
Decentralized control
distribution matching
multiagent systems
optimal transport (OT)
Wasserstein distance
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W