Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning
It addresses the neglected issue of robust communication in multi-agent systems, which is crucial for reliable coordination in applications like robotics or autonomous vehicles, though it is incremental as it builds on existing MACRL frameworks.
The paper tackles the problem of adversarial communication in multi-agent reinforcement learning, showing that many state-of-the-art methods are vulnerable to message attacks, and proposes a game-theoretical method that significantly improves robustness.
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.