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Found 4 papers, 2 papers with code
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios.
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.
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