  While deep neural networks (DNNs) have strengthened the performance of
cooperative multi-agent reinforcement learning (c-MARL), the agent policy can
be easily perturbed by adversarial examples. Considering the safety critical
applications of c-MARL, such as traffic management, power management and
unmanned aerial vehicle control, it is crucial to test the robustness of c-MARL
algorithm before it was deployed in reality. Existing adversarial attacks for
MARL could be used for testing, but is limited to one robustness aspects (e.g.,
reward, state, action), while c-MARL model could be attacked from any aspect.
To overcome the challenge, we propose MARLSafe, the first robustness testing
framework for c-MARL algorithms. First, motivated by Markov Decision Process
(MDP), MARLSafe consider the robustness of c-MARL algorithms comprehensively
from three aspects, namely state robustness, action robustness and reward
robustness. Any c-MARL algorithm must simultaneously satisfy these robustness
aspects to be considered secure. Second, due to the scarceness of c-MARL
attack, we propose c-MARL attacks as robustness testing algorithms from
multiple aspects. Experiments on \textit{SMAC} environment reveals that many
state-of-the-art c-MARL algorithms are of low robustness in all aspect,
pointing out the urgent need to test and enhance robustness of c-MARL
algorithms.
