MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning.

arXiv:2609.33563v2 Announce Type: replace-cross Abstract: World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capture the information needed for future control. We