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Found 6 papers, 3 papers with code
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation
However, existing studies on adversarial attacks are insufficient in both attacking ability and human imperceptibility due to their sole focus on the scope of language.
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
This approach enables agents to form a global consensus from local observations, using it as an additional piece of information to guide collaborative actions during execution.
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing robust MARL methods either approximate or enumerate all possible threat scenarios against worst-case adversaries, leading to computational intensity and reduced robustness.
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
In addition, the proposed framework is model-agnostic and can be applied to most of the current MARL algorithms.
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
First, to benchmark attack naturalness, we contribute the first Physical Attack Naturalness (PAN) dataset with human rating and gaze.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
