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This work aims at constructing a bridge between robust control theory and reinforcement learning. Although, reinforcement learning has shown admirable results in complex control tasks, the agent's learning behavior is opaque. Meanwhile, system theory has several tools for analyzing and controlling dynamical systems. This article places deep Q‐learning is into a control‐oriented perspective to study its learning dynamics with well‐established techniques from robust control. An uncertain linear time‐invariant model is formulated by means of the neural tangent kernel to describe learning. This novel approach allows giving conditions for stability (convergence) of the learning and enables the analysis of the agent's behavior in frequency‐domain. The control‐oriented approach makes it possible to formulate robust controllers that inject dynamical rewards as control input in the loss function to achieve better …