SYMBXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks

2025-05-19

IEEE Conference on Computer Communications

AI Summary

This work addresses the deployment challenge of deep reinforcement learning (DRL) in 6G mobile networks stemming from its lack of interpretability. To this end, the study proposes an intent-driven, programmable control framework that uniquely integrates symbolic artificial intelligence with DRL. By leveraging symbolic rules and logical reasoning, the framework generates semantically rich explanations for agent decisions, substantially enhancing the transparency and controllability of learned behaviors. Evaluated on real-world network management tasks, the approach achieves a 12% improvement in median cumulative reward over pure DRL baselines while producing human-understandable decision processes. This integration establishes a novel paradigm for explainable reinforcement learning (XRL), bridging the gap between high-performance learning and interpretable, trustworthy autonomy in complex communication systems.

Technology Category

• Machine Learning: Reinforcement Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Explainable AI
• Deep Reinforcement Learning
• Mobile Networks
• Symbolic AI
• 6G

Innovation

• Symbolic AI
• Explainable Reinforcement Learning
• Deep Reinforcement Learning
• Intent-based Control
• 6G Networks