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This paper applies meta-learning and pretraining to improve neural stimulation response modeling, reducing catastrophic forecast failures and enhancing prediction accuracy in non-human primate studies.
Introduces GORMPO, a density-regularized offline RL algorithm that uses generative density modeling to restrict policy updates to high-density areas, achieving 17% improvement on a real-world medical dataset and outperforming state-of-the-art baselines.