Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning
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 ...
| Authors | Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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