Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is vulnerable to noises and adversarial attacks, it is crucial to develop robust communicative MARL technique. However, existing research in this domain has predominantly focused on passive defense strategies, where agents receive all messages equally, making it hard to balance performance and robustness. We propose an active defense strategy, where agents automatically reduce the impact of potentially harmful messages on the final decision. There are two challenges to implement this strategy, that are defining unreliable messages and adjusting the unreliable messages' impact on the final decision properly. To address them, we design an Active Defense Multi-Agent Communication framework (ADMAC), which estimates the reliability of received messages and adjusts their impact on the final decision accordingly with the help of a decomposable decision structure. The superiority of ADMAC over existing methods is validated by experiments in three communication-critical tasks under four types of attacks.

翻译：通信在多智能体强化学习（MARL）中被证明能有效促进智能体间的协作。由于现实场景中的通信易受噪声和对抗攻击影响，开发鲁棒的通信式MARL技术至关重要。然而，现有研究主要集中于被动防御策略，即智能体无差别接收所有消息，导致难以平衡性能与鲁棒性。我们提出一种主动防御策略，使智能体自动降低潜在有害消息对最终决策的影响。实施该策略面临两大挑战：定义不可靠消息，以及合理调整不可靠消息对最终决策的影响。为解决这些问题，我们设计了主动防御多智能体通信框架（ADMAC），该框架通过可分解决策结构估计接收消息的可靠性，并据此调整其对最终决策的影响。在三种通信关键任务及四类攻击场景下的实验验证了ADMAC相较现有方法的优越性。