论文标题
多代理增强学习中的稀疏对抗攻击
Sparse Adversarial Attack in Multi-agent Reinforcement Learning
论文作者
论文摘要
合作多代理强化学习（CMARL）具有许多真实的应用程序，但是部署时，现有CMARL算法培训的政策不够强大。关于RL系统的对抗攻击也存在许多方法，这意味着RL系统可能会遭受对抗攻击，但大多数都集中在单个代理RL上。在本文中，我们在CMARL系统上提出了一个\ textit {稀疏对抗攻击}。我们将（MA）RL与正规化一起训练攻击政策。我们的实验表明，当当前CMARL算法训练的政策在团队中只有一名或几个代理（例如，25个或25个中的1个中的1个中的1个）在几个时间段攻击（例如，攻击3个时间段的3个时间段中的3个）时，可以获得较差的性能。
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).
