Open Radio Access Networks (O-RAN) are increasingly adopting data-driven control through Deep Reinforcement Learning (DRL) to optimize complex tasks such as network slicing and mobility management. However, the deployment of DRL in carrier-grade networks is hindered by its inherent opacity and stochastic execution, which limit operator trust, auditability, and safe deployment. Existing explainable AI (XAI) approaches primarily provide post-hoc insights and fail to produce executable, interpretable policies suitable for operational environments. In this paper, we present DeRAN, a neuro-symbolic framework that bridges the gap between DRL performance and operational transparency by distilling black-box DRL policies into human-readable symbolic representations. DeRAN introduces a concept-driven abstraction layer that transforms high-dimensional network telemetry into a compact set of semantically meaningful features, enabling interpretable policy learning. Building on the semantically grounded concepts, DeRAN synthesizes symbolic policies using deep symbolic regression (DSR) for continuous control and neurally guided differentiable logic (NUDGE) for discrete decision-making. We implement DeRAN on a live 5G O-RAN testbed and evaluate it on two representative use cases. Experimental results demonstrate that DeRAN achieves 78% and 87% of DRL's cumulative rewards in the two use cases, while offering interpretability and auditability by design. Source code is available at https://github.com/Jadejavu/DeRAN

翻译：开放无线接入网（O-RAN）正越来越多地采用基于深度强化学习（DRL）的数据驱动控制，以优化网络切片和移动性管理等复杂任务。然而，DRL在运营商级网络中的部署因其固有的不透明性和随机执行而受到阻碍，这限制了运营商的信任度、可审计性和安全部署。现有的可解释人工智能（XAI）方法主要提供事后洞察，无法生成适用于运营环境的可执行、可解释策略。在本文中，我们提出DeRAN——一种神经符号框架，通过将黑箱DRL策略提炼为人类可读的符号表示，弥合DRL性能与运营透明度之间的差距。DeRAN引入了一个概念驱动的抽象层，将高维网络遥测数据转化为一组紧凑的语义有意义的特征，从而实现可解释的策略学习。基于语义基础概念，DeRAN通过深度符号回归（DSR）合成连续控制的符号策略，并通过神经引导可微逻辑（NUDGE）合成离散决策的策略。我们在一个实时5G O-RAN测试平台上实现DeRAN，并在两个代表性用例上进行评估。实验结果表明，DeRAN在两个用例中分别达到DRL累积奖励的78%和87%，同时通过设计提供可解释性和可审计性。源代码可在https://github.com/Jadejavu/DeRAN获取。