Interactive Explanation and Steering of DRL Agents for Massive MIMO Scheduling with SYMBXRL

Abstract

Future 6th-generation (6G) mobile networks will increasingly rely on Deep Reinforcement Learning (DRL) for real-time decision optimization. However, DRL’s opaque nature hinders its adoption, as operators need to understand and control these complex systems, necessitating explainability tools to reveal the model’s reasoning. This paper demonstrates SYMBXRL, an EXplainable Reinforcement Learning ( XRL) framework that translates DRL’s internal logic into human-interpretable symbolic representations and enables intent-based action steering. We introduce a novel interactive dashboard that enhances transparency and control by providing a real-time view of the DRL agent’s operation. Our demonstration showcases how SYM- BXRL i) generates human-readable explanations using symbolic Artificial Intelligence (AI) and knowledge graphs, (ii) enables operator-defined, intent-based action steering for performance improvement, and (iii) provides real-time visualization of agent behavior and network metrics. We demonstrate SYMBXRL using a DRL agent that schedules users in a Massive MIMO scenario, leveraging real-world channel measurements from a 64-antenna testbed to maximize spectral efficiency while maintaining fairness.