PatMask: Pluggable Adversarial Topology Masking for Multi-Agent Reinforcement Learning under Communication Disconnection

Cooperative Multi-Agent Reinforcement Learning (MARL) relies on communication to mitigate limited local observations. However, existing methods are vulnerable to communication disconnection. To address this, we propose PatMask, a pluggable adversarial topology masking mechanism to enhance the robustness of existing MARL methods under diverse communication disconnections. Specifically, PatMask introduces an adversarial mask generator that adaptively disrupts critical communication links during training. We establish a contraction guarantee for robust policy evaluation under topology perturbations and develop an alternating adversarial training procedure. Extensive evaluations on various cooperative benchmarks show that PatMask can maintain high win rate and high coordination efficiency across various environments under controlled communication disconnection. Compared with baselines trained without additional disconnection augmentation, PatMask achieves 15.7-41.6 percentage-point higher win rates with only 41% of the training steps and less than 77% of the communication cost. Moreover, trained PatMask policies can generalize without retraining across the evaluated disconnection types and probabilities.