Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems

2026-05-12

AI Summary

This work addresses the challenge of response mapping failure in large-scale multi-agent systems caused by dynamic population changes, which traditional planners struggle to handle. The authors propose a population-aware coordination interface grounded in a Lagrangian relaxation framework, employing conditional neural networks to learn primal and dual mappings from compact population summaries. This enables stable cross-episode predictions without retraining, effectively adapting to evolving populations. The approach efficiently coordinates massive agent populations (e.g., 500,000 agents) using only small subsamples (e.g., 20,000 agents) and reformulates Sim2Real transfer into a backtestable pipeline. Evaluated on a supply chain capacity control task, the method reduces prediction error by 16–19% and capacity violations by 20–51% compared to baselines. When applied to real-world data, the simulation-trained model achieves a MAPE of 11.1%, substantially outperforming baseline methods reporting 13–24%.

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.

Problem

• multi-agent systems
• resource constraints
• population composition shift
• coordination
• Lagrangian relaxation

Innovation

• population-aware coordination
• Lagrangian relaxation
• primal-dual maps
• multi-agent systems
• Sim2Real transfer