Traditional approaches to DFA inference stem from symbolic AI, including both active learning methods (e.g., Angluin’s L* algorithm and its variants) and passive techniques (e.g., Biermann and Feldman’s method, RPNI). Meanwhile, sub-symbolic AI, particularly machine learning, offers alternative paradigms for learning from data, such as supervised, unsupervised, and reinforcement learning (RL). In this paper, we investigate the use of Q-learning, a well-known reinforcement learning algorithm, for the passive inference of deterministic finite automata. Our core insight is that the learned Q-function, which maps state-action pairs to rewards, can be reinterpreted as the transition function of a DFA over a finite domain. This provides a novel bridge between sub-symbolic learning and symbolic representations. We demonstrate how Q-learning can be adapted for automaton inference and provide an evaluation on several examples.