  Recently, many cooperative distributed multi-agent reinforcement learning
(MARL) algorithms have been proposed in the literature. In this work, we study
the effect of adversarial attacks on a network that employs a consensus-based
MARL algorithm. We show that an adversarial agent can persuade all the other
agents in the network to implement policies that optimize an objective that it
desires. In this sense, the standard consensus-based MARL algorithms are
fragile to attacks.
