Explainability is vital in intrusion detection, as it clarifies the reasoning behind system predictions, especially when misclassifications could cause serious security breaches. By making model decisions interpretable, explainability facilitates debugging and continuous improvement. This paper advances the field by focusing on explainability and interpretability in Machine Learning (ML), particularly within Reinforcement Learning (RL). We propose a decentralized Multi-Agent Reinforcement Learning (MARL) framework with a hybrid architecture that integrates logical reasoning to design an explainable Intrusion Detection System (IDS). Logical rules provide agents with prior knowledge of their environment, improving learning efficiency and decision transparency and enhancing overall performance and reliability. The model, evaluated on the NSL-KDD benchmark dataset, achieves a 99% detection rate, demonstrating strong robustness and effectiveness.