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arXiv · 2403.08906

Strategizing against Q-learners: A Control-theoretical Approach

Yuksel Arslantas·Ege Yuceel·Muhammed O. Sayin
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Abstract

In this paper, we explore the susceptibility of the independent Q-learning algorithms (a classical and widely used multi-agent reinforcement learning method) to strategic manipulation of sophisticated opponents in normal-form games played repeatedly. We quantify how much strategically sophisticated agents can exploit naive Q-learners if they know the opponents' Q-learning algorithm. To this end, we formulate the strategic actors' interactions as a stochastic game (whose state encompasses Q-function estimates of the Q-learners) as if the Q-learning algorithms are the underlying dynamical system. We also present a quantization-based approximation scheme to tackle the continuum state space and analyze its performance for two competing strategic actors and a single strategic actor both analytically and numerically.

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Yuksel Arslantas, Ege Yuceel, Muhammed O. Sayin. 2024-03-13. Strategizing against Q-learners: A Control-theoretical Approach. https://doi.org/10.1109/lcsys.2024.3416240

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