In the DRL case, mimic learning aims to replace the closedbox successfully applied in challenging tasks, their application in realworld DRL controller with an interpretable one, able to mimic the operational settings is challenged by methods' limited ability decisions made by the former [3, 19, 35]. A mimic learner tries to to provide explanations. Among the paradigms for explainability in optimize fidelity [35], which is determined by comparing the mimic DRL is the interpretable box design paradigm, where interpretable controller's actions with the actions selected by the DRL model. To models substitute inner constituent models of the DRL method, thus extract knowledge from deep neural networks, recent work [3, 19] making the DRL method "inherently" interpretable. In this paper has applied mimic learning with tree representations, using decision we explore this paradigm and we propose XDQN, an explainable trees: Criteria used for splitting tree nodes provide a tractable way variation of DQN, which uses an interpretable policy model trained to explain the predictions made by the controller.