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Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards
In this paper, we propose to automatically generate goal-consistent intrinsic rewards for the agent to learn, by maximizing which the expected accumulative extrinsic rewards can be maximized.
DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks
In this paper, we propose an interpretable white-box AE attack approach, DI-AA, which explores the application of the interpretable approach of the deep Taylor decomposition in the selection of the most contributing features and adopts the Lagrangian relaxation optimization of the logit output and L_p norm to further decrease the perturbation.
IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks
To further make the perturbations more imperceptible, we propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly, which can restrict the total perturbations and perturbation points simultaneously.
Generalizing Adversarial Examples by AdaBelief Optimizer
Recent research has proved that deep neural networks (DNNs) are vulnerable to adversarial examples, the legitimate input added with imperceptible and well-designed perturbations can fool DNNs easily in the testing stage.
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
In this paper, we consider the problem of adaptively utilizing a given shaping reward function.
Multi-Agent Deep Reinforcement Learning with Adaptive Policies
To tackle this, we propose to train multiple policies for each agent and postpone the selection of the best policy at execution time.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
