Sparse Adversarial Attack in Multi-agent Reinforcement Learning

2022-05-19 · Yizheng Hu, Zhihua Zhang

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).

📄 PDF Abstract BibTeX arXiv:2205.09362

Code (0)

등록된 구현이 없습니다.

Tasks

Adversarial Attack Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Enhancing the Robustness of QMIX against State-adversarial Attacks

2023-07-03 · Weiran Guo, Guanjun Liu, Ziyuan Zhou, Ling Wang 외

Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforceme…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Adversarial attacks in consensus-based multi-agent reinforcement learning

2021-03-11 · Martin Figura, Krishna Chaitanya Kosaraju, Vijay Gupta

Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature. In this work, we study the effect of adversarial attacks on a network that employs a conse…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning

2025-02-05 · Sunwoo Lee, Jaebak Hwang, Yonghyeon Jo, Seungyul Han

Traditional robust methods in multi-agent reinforcement learning (MARL) often struggle against coordinated adversarial attacks in cooperative scenarios. To address this limitation, we propose the Wolfpack Adversarial Att…

Adversarial Attack Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Sparse Black-box Video Attack with Reinforcement Learning

2020-01-11 · Xingxing Wei, Huanqian Yan, Bo Li

Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To overcome this drawback, a few methods tr…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Video Recognition

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

2023-01-11 · Maxwell Standen, Junae Kim, Claudia Szabo

Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use …

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1