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Found 4 papers, 1 papers with code
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization
Then we generalize a complexity-related variable, which is sensitive to the changes in model width and the trade-off factors in adversarial training.
ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning
Then, to mitigate the vulnerability of existing GCRL algorithms, we introduce Adversarial Representation Tactics, which combines Semi-Contrastive Adversarial Augmentation with Sensitivity-Aware Regularizer to improve the adversarial robustness of the underlying RL agent against various types of perturbations.
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking
In this paper, we propose a novel adversarial attack method to generate noises for single object tracking under black-box settings, where perturbations are merely added on initial frames of tracking sequences, which is difficult to be noticed from the perspective of a whole video clip.
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL
Transferring knowledge among various environments is important to efficiently learn multiple tasks online.
