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MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
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 study whether self-supervised joint-embedding prediction (JEPA) can provide this learning signal f
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