  Recent studies in multi-agent communicative reinforcement learning (MACRL)
have demonstrated that multi-agent coordination can be greatly improved by
allowing communication between agents. Meanwhile, adversarial machine learning
(ML) has shown that ML models are vulnerable to attacks. Despite the increasing
concern about the robustness of ML algorithms, how to achieve robust
communication in multi-agent reinforcement learning has been largely neglected.
In this paper, we systematically explore the problem of adversarial
communication in MACRL. Our main contributions are threefold. First, we propose
an effective method to perform attacks in MACRL, by learning a model to
generate optimal malicious messages. Second, we develop a defence method based
on message reconstruction, to maintain multi-agent coordination under message
attacks. Third, we formulate the adversarial communication problem as a
two-player zero-sum game and propose a game-theoretical method R-MACRL to
improve the worst-case defending performance. Empirical results demonstrate
that many state-of-the-art MACRL methods are vulnerable to message attacks, and
our method can significantly improve their robustness.
