mlsanityData to 14 Oct 2026

Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation

arXiv 2509.14925 ↗First posted 18 Sep 2025cs.LG cs.NIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 29 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

13 of 29 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  4. 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
  5. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  6. 2016Not Just a Black Box: Learning Important Features Through Propagating Activation Differencescited by 297
  7. 2017Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learningcited by 77
  8. 2018Toward Interpretable Deep Reinforcement Learning with Linear Model U-Treescited by 65
  9. 2019Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learningcited by 60
  10. 2023Explaining Reinforcement Learning with Shapley Valuescited by 26
  11. 2022C-SENN: Contrastive Self-Explaining Neural Networkcited by 9
  12. 2023Q-SENN: Quantized Self-Explaining Neural Networkscited by 9
1 more
  1. 2025Explainable Reinforcement Learning via Temporal Policy Decompositioncited by 5

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  1. 2026SymbXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks
  2. 2025Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
  3. 2026SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control
  4. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
  5. 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
  6. 2025Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
  7. 2022Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
  8. 2025An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing