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 for multi-agent reinforcement learning. We introduce MA-JEPA, a stochastic world model that replaces observation reconstruction with prediction of target representations, enabling model-based multi-agent reinforcement learning with centralized training and decentralized execution. A categorical latent state and a causal Transformer are trained with posterior and action-conditioned dynamics predi...
| Authors | Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de Witt |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 8 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.