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arXiv AI By Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de Witt

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

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MA-JEPA is a stochastic world model designed for multi‑agent reinforcement learning that replaces observation reconstruction with joint‑embedding prediction. It uses a categorical latent state and a causal Transformer trained on posterior and action‑conditioned dynamics to generate imagined trajectories for actor‑critic learning. The model achieves strong performance on the SMAC benchmark, matching or surpassing the best reported mean win rates on half of the evaluated maps.

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