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Found 2 papers, 1 papers with code
Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
We introduce a novel framework for generalized Gaussian error modeling in deep reinforcement learning to enhance the flexibility of error distribution modeling by incorporating additional higher-order moment, particularly kurtosis, thereby improving the estimation and mitigation of data-dependent aleatoric uncertainty.
Towards Understanding the Relationship between In-context Learning and Compositional Generalization
This indicates the usefulness of in-context learning problems as an inductive bias for generalization.
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