Offline Multi-agent Continual Cooperation via Skill Partition and Reuse
Extracting skills from multi-agent offline dataset improves learning efficiency via sharing task-invariant coordination skills among tasks. In settings where tasks occur sequentially and the space of skills grows exponentially, existing approaches that rely on heuristically designed and fixed-sized skill libraries struggle to resolve the problem of distributional shift and interference, facing catastrophic forgetting and plasticity loss. To address this problem and endow agents with the ability to continually discover and reuse coordination skills in open-environment, we propose COMAD, a principled framework for Continual Offline Multi-agent Skill Discovery via Skill Partition and Reuse. ...
| Authors | Yuchen Xiao, Lei Yuan, Ruiqi Xue et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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