SYMBXRL: Symbolic Explainable Deep Reinforcement Learning for Mobile Networks

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.