Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

Konrad Nowosadko; Franco Ruggeri; Ahmad Terra

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arXiv · 2509.14925

Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

Konrad Nowosadko·Franco Ruggeri·Ahmad Terra

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Abstract

Deep reinforcement learning (DRL) methods, though powerful, often lack transparency, which limits their adoption in critical domains. We apply Self-Explaining Neural Networks (SENNs) to RL by parametrizing the policy of a PPO agent with a SENN, producing intrinsic local explanations, and propose a method for aggregating them into global explanations. We evaluate our approach on a mobile network resource allocation problem, our approach performs within a small margin of the state-of-the-art deep learning method and significantly outperforms the best deployed heuristic, while the extracted global explanations correlate strongly with DeepLift and InputXGradient, making SENNs a promising candidate for high-stakes RL.

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Konrad Nowosadko, Franco Ruggeri, Ahmad Terra. 2025-09-18. Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation. https://arxiv.org/abs/2509.14925

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