Cooperative multi-agent reinforcement learning (cMARL) has many real applications, but the policy trained by existing cMARL algorithms is not robust enough when deployed. There exist also many methods about adversarial attacks on the RL system, which implies that the RL system can suffer from adversarial attacks, but most of them focused on single agent RL. In this paper, we propose a \textit{sparse adversarial attack} on cMARL systems. We use (MA)RL with regularization to train the attack policy. Our experiments show that the policy trained by the current cMARL algorithm can obtain poor performance when only one or a few agents in the team (e.g., 1 of 8 or 5 of 25) were attacked at a few timesteps (e.g., attack 3 of total 40 timesteps).

翻译：合作性多剂强化学习(cMARL)有许多实际应用,但现有的CMARL算法所培训的政策在部署时不够有力。还有关于对RL系统进行对抗性攻击的许多方法,这意味着RL系统可能遭受对抗性攻击,但其中多数侧重于单一剂RL。在本文中,我们建议对CMARL系统进行“textit{sparse 对抗性攻击 ” 。我们用(MA)RL进行正规化来训练攻击政策。我们的实验表明,当小组中只有一名或数名特工(例如,8名或25名中的5名特工)在几时受到攻击时(例如,总共40个步骤中的3次攻击),由目前的CMARL算法所培训的政策可以取得不良的性能。