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Found 3 papers, 1 papers with code
Sample Efficient Reward Augmentation in offline-to-online Reinforcement Learning
Offline-to-online RL can make full use of pre-collected offline datasets to initialize policies, resulting in higher sample efficiency and better performance compared to only using online algorithms alone for policy training.
New Designed Loss Functions to Solve Ordinary Differential Equations with Artificial Neural Network
This paper investigates the use of artificial neural networks (ANNs) to solve differential equations (DEs) and the construction of the loss function which meets both differential equation and its initial/boundary condition of a certain DE.
A Bayesian Robust Regression Method for Corrupted Data Reconstruction
Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task.
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