Efficient Adversarial Attacks on Online Multi-agent Reinforcement
  Learning
Guanlin Liu Lifeng Lai
Due to the broad range of applications of multi-agent reinforcement learning
(MARL), understanding the effects of adversarial attacks against MARL model is
essential for the safe applications of this model. Motivated by this, we
investigate the impact of adversarial attacks on MARL. In the considered setup,
there is an exogenous attacker who is able to modify the rewards before the
agents receive them or manipulate the actions before the environment receives
them. The attacker aims to guide each agent into a target policy or maximize
the cumulative rewards under some specific reward function chosen by the
attacker, while minimizing the amount of manipulation on feedback and action.
We first show the limitations of the action poisoning only attacks and the
reward poisoning only attacks. We then introduce a mixed attack strategy with
both the action poisoning and the reward poisoning. We show that the mixed
attack strategy can efficiently attack MARL agents even if the attacker has no
prior information about the underlying environment and the agents' algorithms.