Abstract:In large-scale multi-agent systems with shared resource constraints, an upstream planner must iteratively evaluate candidate resource plans -- assessing feasibility, aggregate response, and marginal cost -- before committing to one. Lagrangian relaxation separates local decisions through a broadcast cost signal, but the planner still needs the cost-to-utilization response map to explore plan space, and this map depends on population composition that changes across planning cycles. We propose \emph{population-aware coordination interfaces}: learned primal and dual maps, conditioned on compact population summaries, that the planner queries inside its iterative loop. The primal map predicts aggregate utilization under a proposed cost trajectory; the dual map predicts the cost trajectory for a target plan. By encoding response-relevant population structure, these maps remain reliable across evolving populations without per-cycle retraining, and support coordination of large populations from compact subsamples. We additionally cast Sim2Real transfer as a backtestable procedure, enabling evaluation before deployment. In a supply-chain capacity-control case study, population-aware interfaces reduce forecast error by 16--19\% and capacity violations by 20--51\% relative to population-unaware baselines under composition shift; 20K-agent cohorts support accurate coordination of 500K-agent populations; and simulator-trained primal maps achieve 11.1\% MAPE on real observations versus 13--24\% for baselines.
| Comments: | 30 pages, 16 figures. Submitted to NeurIPS 2026 |
| Subjects: | Multiagent Systems (cs.MA); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13900 [cs.MA] |
| (or arXiv:2605.13900v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13900 arXiv-issued DOI via DataCite |
Submission history
From: Dominique Perrault-Joncas [view email]
[v1]
Tue, 12 May 2026 16:57:24 UTC (2,153 KB)