… to leverage adversarial training to achieve AE defense. Formally, given the training set D
= {(x1,y1), ··· , (xn,yn)} and an NN Fθ, the objective of traditional adversarial training can be …

… of long-term rewards, or train the agents and RL-based … of the training process. In this
work, we propose a strong and efficient robust training framework for RL, named Worst-case-aware …

… the “worst-case-aware decoder," which indirectly assesses potential worst case scenario
that might emerge from pseudo-label training. We employ adversarial training of the encoder to …

… and adversarial robustness generalization by performing adversarial training under weight
… AWP in the adversarial attack scheme, where we optimize adversarial examples under the …

… Adversarial training (AT) has attracted widespread attention for its efficacy in enhancing the
… and adversarial examples, while the inner maximization generates adversarial examples by …

… -caseaware training strategy named Adversarial Training with … We first present a novel
adversarial training approach based … noise injection training and adversarial training to establish …

… -caseaware training strategy named Adversarial Training with … We first present a novel
adversarial training approach based … noise injection training and adversarial training to establish …

… This paper introduces a worst-case-aware training algorithm designed to improve the
adversarial robustness of the Decision Transformer, namely Adversarially Robust Decision …

… In this work, we propose Consistent123, a case-aware two-… Consequently, we embed
a case-aware CLIP-based detection … During the first-stage training, we conduct boundary …

… Huang, “Efficient adversarial training without attacking: Worst-case-aware robust reinforcement
learning,” Advances in Neural Information Processing Systems, vol. 35, pp. 22547– …