mlsanityData to 14 Oct 2026

SymbXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks

arXiv 2601.22024 ↗First posted 29 Jan 2026cs.NI cs.AIcited by 2

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

Abstract (arXiv)

The operation of future 6th-generation (6G) mobile networks will increasingly rely on the ability of deep reinforcement learning (DRL) to optimize network decisions in real-time. DRL yields demonstrated efficacy in various resource allocation problems, such as joint decisions on user scheduling and antenna allocation or simultaneous control of computing resources and modulation. However, trained DRL agents are closed-boxes and inherently difficult to explain, which hinders their adoption in production settings. In this paper, we make a step towards removing this critical barrier by presenting SymbXRL, a novel technique for explainable reinforcement learning (XRL) that synthesizes human-interpretable explanations for DRL agents. SymbXRL leverages symbolic AI to produce explanations where key concepts and their relationships are described via intuitive symbols and rules; coupling such a representation with logical reasoning exposes the decision process of DRL agents and offers more comprehensible descriptions of their behaviors compared to existing approaches. We validate SymbXRL in practical network management use cases supported by DRL, proving that it not only improves the semantics of the explanations but also paves the way for explicit agent control: for instance, it enables intent-based programmatic action steering that improves by 12% the median cumulative reward over a pure DRL solution.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 59 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

21 of 59 references.

References · 21

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2017Safe Reinforcement Learning via Shieldingcited by 449
  6. 2018Programmatically Interpretable Reinforcement Learningcited by 193
  7. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  8. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
  9. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 153
  10. 2020Explainable Reinforcement Learning: A Surveycited by 141
  11. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 133
  12. 2020Explainability in Deep Reinforcement Learningcited by 97
9 more
  1. 2020Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitionscited by 41
  2. 2021Generalized Proximal Policy Optimization with Sample Reusecited by 40
  3. 2021ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platformscited by 35
  4. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  5. 2023EXPLORA: AI/ML EXPLainability for the Open RANcited by 13
  6. 2021Learning Symbolic Rules for Interpretable Deep Reinforcement Learningcited by 9
  7. 2019When Machine Learning Meets Wireless Cellular Networks: Deployment, Challenges, and Applicationscited by 6
  8. 2023Common Challenges of Deep Reinforcement Learning Applications Development: An Empirical Studycited by 5
  9. 2022Reinforcement Learning-based Joint User Scheduling and Link Configuration in Millimeter-wave Networkscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networkscited by 0
  2. 2026Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control
  2. 2025Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
  3. 2022Decisions that Explain Themselves: A User-Centric Deep Reinforcement Learning Explanation System
  4. 2026Analyzing Symbolic Properties for DRL Agents in Systems and Networking
  5. 2025A Survey on Explainable Deep Reinforcement Learning
  6. 2025A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs
  7. 2025Explainable AI-aided Feature Selection and Model Reduction for DRL-based V2X Resource Allocation
  8. 2025Explainable Reinforcement Learning Agents Using World Models