aiqbench analysis
This research addresses the critical challenge of zero-shot generalization in offline multi-agent reinforcement learning by demonstrating that scaling task diversity is more impactful than simply increasing dataset size. By extending sequence modeling architectures to handle variable agent counts and multi-task spaces, the study achieves a 3.2x improvement on held-out tasks across four complex environments. These findings provide a clear roadmap for building robust MARL agents capable of transferring knowledge to unseen scenarios without relying on massive data volumes.
Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning
arXiv:2609.03667v1 Announce Type: cross Abstract: Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning (MARL). In this work, we present a principled analysis of zero-shot task generalisation in the offline setting and conduct an extensive empirical investigation into the scaling behaviour governing task diversity, dataset size, and network capacity. To facilitate this study, we extend offline sequence modelling architectures to handle multi-task observation and action spaces alongside variable agent counts across tasks. Our primary finding is that scaling task diversity---rather than sheer dataset size is the dominant factor in achieving robust zero-shot transfer. Through large-scale experiments across four challenging environments (Connector, RWARE, SMAX, and LBF), we demonstrate that our multi-task approach achieves a mean improvement of 3.2x on held-out test tasks compared to single-task models and consistently outperforms strong behaviour cloning baselines. These results suggest that the development of generalisable MARL agents should prioritise the diversity of the training distribution with varying numbers of agents, providing a roadmap for scaling offline MARL effectively.
Abstract:Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning (MARL). In this work, we present a principled analysis of zero-shot task generalisation in the offline setting and conduct an extensive empirical investigation into the scaling behaviour governing task diversity, dataset size, and network capacity. To facilitate this study, we extend offline sequence modelling architectures to handle multi-task observation and action spaces alongside variable agent counts across tasks. Our primary finding is that scaling task diversity---rather than sheer dataset size is the dominant factor in achieving robust zero-shot transfer. Through large-scale experiments across four challenging environments (Connector, RWARE, SMAX, and LBF), we demonstrate that our multi-task approach achieves a mean improvement of 3.2x on held-out test tasks compared to single-task models and consistently outperforms strong behaviour cloning baselines. These results suggest that the development of generalisable MARL agents should prioritise the diversity of the training distribution with varying numbers of agents, providing a roadmap for scaling offline MARL effectively.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.03667 [cs.LG]
(or arXiv:2609.03667v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.03667 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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