Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

Abstract--Reinforcement Learning (RL) methods that incorporate deep neural networks (DNN), though powerful, often lack transparency. Their black-box characteristic hinders inter-pretability and reduces trustworthiness, particularly in critical domains. T o address this challenge in RL tasks, we propose a solution based on Self-Explaining Neural Networks (SENNs) along with explanation extraction methods to enhance inter-pretability while maintaining predictive accuracy. Our approach targets low-dimensionality problems to generate robust local and global explanations of the model's behaviour . We evaluate the proposed method on the resource allocation problem in mobile networks, demonstrating that SENNs can constitute interpretable solutions with competitive performance. This work highlights the potential of SENNs to improve transparency and trust in AIdriven decision-making for low-dimensional tasks. Interest in Explainable Artificial Intelligance (XAI) has been rapidly growing, facilitated by the need for transparency. Although powerful, Deep Neural Networks (DNNs) models often operate as black boxes, making it difficult to interpret their decisions, leading to a lack of trust among stakeholders and consequently hindering their applicability.