Single-agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning

Fan Yao, Yuwei Cheng, Ermin Wei, Haifeng Xu

Keywords: Multi-agent learning, reward poisoning attack, Nash equilibrium, monotone game, convergence, robustness

TL;DR: Characterize efficiency and robustness trade-off for multi-agent learning algorithms

Abstract: We investigate the robustness of multi-agent learning in strongly monotone games with bandit feedback. While previous research has developed learning algorithms that achieve last-iterate convergence to the unique Nash equilibrium (NE) at a polynomial rate, we demonstrate that all such algorithms are vulnerable to adversaries capable of poisoning even a single agent's utility observations. Specifically, we propose an attacking strategy such that for any given time horizon $T$, the adversary can mislead any multi-agent learning algorithm to converge to a point other than the unique NE with a corruption budget that grows sublinearly in $T$. To further understand the inherent robustness of these algorithms, we characterize the fundamental trade-off between convergence speed and the maximum tolerable total utility corruptions for two example algorithms, including the state-of-the-art one. Our theoretical and empirical results reveal an intrinsic efficiency-robustness trade-off: the faster an algorithm converges, the more vulnerable it becomes to utility poisoning attacks. To the best of our knowledge, this is the first work to identify and characterize such a trade-off in the context of multi-agent learning.

Primary Area: learning on time series and dynamical systems

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Submission Number: 8335