Multi-Dimensional Perturbation Strategies for Adversarial Attacks in Multi-Agent Deep Reinforcement Learning

Abstract

Research indicates that single-agent reinforcement learning is vulnerable to adversarial attacks, which can lead to decision-making errors. Similarly, multi-agent deep reinforcement learning (MADRL) systems face analogous adversarial threats. However, existing attack methods require substantial investment in agent design and computational resources, limiting the feasibility of such attacks. To address this issue, we reformulate adversarial attacks as an optimization problem and propose the MREFDW-GA algorithm, which integrates dimension-weighted perturbations and a multi-stage robustness evaluation function. This approach combines dimension-weighted perturbations with a multi-stage robustness evaluation function, thereby enhancing the efficiency of evolutionary algorithms while dynamically adjusting search strategies to escape local optima. Experimental results demonstrate that this method can effectively execute black-box attacks by iteratively generating adversarial perturbations, significantly degrading the performance of MADRL systems and opening new research avenues for efficient black-box attacks.