Cooperating with Future Collaborators: Multi-Agent RL under Staggered Participation

2026-10-05

AI Summary

This study addresses the challenge of cross-temporal and cross-agent learning dependencies induced by staggered participation mechanisms in multi-agent reinforcement learning. To tackle this, we propose SPL, a training enhancement framework that introduces the first training-time data augmentation mechanism tailored for such scenarios. By integrating lookahead supervision with outcome guidance, SPL decouples information identification from utilization, thereby synergistically optimizing early-stage information acquisition and late-stage decision-making policies. Extensive evaluations on the MPE and RWARE benchmarks, as well as in an eight-agent UAV-UGV physical environment, demonstrate the effectiveness of our approach. Across sixty comparative experiments, SPL achieves an average improvement of 14.1% in task completion rate, effectively resolving the collaboration bottlenecks inherent in staggered participation settings.

Abstract

In cooperative Multi-Agent Reinforcement Learning (MARL), agents are often trained under concurrent participation, while in many tasks some agents act earlier and leave task-relevant information that becomes useful to agents participating later. We study this setting as staggered participation (SP), which introduces a cross-time, cross-agent learning dependency because an early action may affect the return through the information it provides and the later policy that uses it. Learning under SP therefore requires both identifying what information is useful for future decisions and learning how later agents should use it. We propose Staggered Participation Learning (SPL), a training-time augmentation that addresses these two parts with prospective acquisition supervision for earlier agents and outcome-supervised receiver learning for later agents. We evaluate SPL across multiple policy-based MARL backbones, environments, and staggered-participation patterns. Across 60 MPE/RWARE backbone setting comparisons, SPL achieves higher observed mean task completion in every case, with an average difference of 14.1%. The gains also extend to eight-agent teams and a physics-based UAV-UGV environment in Isaac Lab, providing evidence across algorithmic, temporal, and embodied settings.

Problem

• Multi-Agent Reinforcement Learning
• Staggered Participation
• Cooperative Learning
• Cross-time Dependency

Innovation

• Multi-Agent Reinforcement Learning
• Staggered Participation
• Prospective Acquisition Supervision
• Outcome-Supervised Receiver Learning
• Cross-time Dependency