Lack of explainability is a key factor limiting the practical adoption of high-performant Deep Reinforcement Learning (DRL) controllers. Explainable RL for networking hitherto used salient input features to interpret a controller's behavior. However, these feature-based solutions do not completely explain the controller's decision-making process. Often, operators are interested in understanding the impact of a controller's actions on performance in the future, which feature-based solutions cannot capture. In this paper, we present CrystalBox, a framework that explains a controller's behavior in terms of the future impact on key network performance metrics. CrystalBox employs a novel learning-based approach to generate succinct and expressive explanations. We use reward components of the DRL network controller, which are key performance metrics meaningful to operators, as the basis for explanations. CrystalBox is generalizable and can work across both discrete and continuous control environments without any changes to the controller or the DRL workflow. Using adaptive bitrate streaming and congestion control, we demonstrate CrytalBox's ability to generate high-fidelity future-based explanations. We additionally present three practical use cases of CrystalBox: cross-state explainability, guided reward design, and network observability.

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