Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

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.

Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2509.14925 [cs.LG]

(or arXiv:2509.14925v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2509.14925 Focus to learn more arXiv-issued DOI via DataCite

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