Strategizing against Q-learners: A Control-theoretical Approach

• URL: http://arxiv.org/abs/2403.08906v3
• Date: Tue, 16 Jul 2024 07:13:25 GMT
• Title: Strategizing against Q-learners: A Control-theoretical Approach
• Authors: Yuksel Arslantas, Ege Yuceel, Muhammed O. Sayin,
• Abstract summary: We quantify how much strategically sophisticated agents can exploit naive Q-learners if they know the opponents' Q-learning algorithm. We present a quantization-based approximation scheme to tackle the continuum state space.
• Score: 1.3927943269211591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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