Attacking c-marl more effectively: A data driven approach
In recent years, a proliferation of methods were developed for cooperative multi-agent
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
[PDF][PDF] Evaluating robustness of cooperative MARL: A model-based approach
In recent years, a proliferation of methods were developed for cooperative multi-agent
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
c-MBA: Adversarial attack for cooperative MARL using learned dynamics model
In recent years, a proliferation of methods were developed for cooperative multi-agent
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
reinforcement learning (c-MARL). However, the robustness of c-MARL agents against …
Sparse adversarial attack in multi-agent reinforcement learning
Y Hu, Z Zhang - arXiv preprint arXiv:2205.09362, 2022 - arxiv.org
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 …
policy trained by existing cMARL algorithms is not robust enough when deployed. There …
Towards comprehensive testing on the robustness of cooperative multi-agent reinforcement learning
While deep neural networks (DNNs) have strengthened the performance of cooperative
multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by …
multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by …
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems
Cooperative Multi-Agent Reinforcement Learning (CMARL) strategies are well known to be
vulnerable to adversarial perturbations. Previous works on adversarial attacks have …
vulnerable to adversarial perturbations. Previous works on adversarial attacks have …
Sok: Adversarial machine learning attacks and defences in multi-agent reinforcement learning
M Standen, J Kim, C Szabo - arXiv preprint arXiv:2301.04299, 2023 - arxiv.org
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 …
(AML) attacks and needs adequate defences before it can be used in real world …
Adversarial attacks on cooperative multi-agent deep reinforcement learning: a dynamic group-based adversarial example transferability method
L Zan, X Zhu, ZL Hu - Complex & Intelligent Systems, 2023 - Springer
Existing research shows that cooperative multi-agent deep reinforcement learning (c-
MADRL) is vulnerable to adversarial attacks, and c-MADRL is increasingly being applied to …
MADRL) is vulnerable to adversarial attacks, and c-MADRL is increasingly being applied to …
Robustness testing for multi-agent reinforcement learning: State perturbations on critical agents
Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such
as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are …
as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are …
Model and method: Training-time attack for cooperative multi-agent reinforcement learning
The robustness of deep cooperative multi-agent reinforcement learning (MARL) is of great
concern and limits the application to real-world risk-sensitive tasks. Adversarial attack is a …
concern and limits the application to real-world risk-sensitive tasks. Adversarial attack is a …