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cs.LG2026
MA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
Brandon Gary Kaplowitz, Osaze James Obahor, Christian Schroeder de Witt
World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capture the information needed f…
cs.LG2023★ 1 cited
Bayesian Exploration Networks
Mattie Fellows, Brandon Kaplowitz, Christian Schroeder de Witt +1
Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an explora…