SIA: Symbolic Interpretability for Anticipatory Deep Reinforcement Learning in Network Control

Abstract

Deep Reinforcement Learning (DRL) promises adap- tive control for future mobile networks but conventional agents remain reactive: they act on past and current measurements and cannot leverage short-term forecasts of exogenous Key Performance Indicators ( KPIs) such as bandwidth. Augmenting agents with predictions can overcome this temporal myopia, yet uptake in networking is scarce because forecast-aware agents act as closed-boxes; operators cannot tell whether predictions guide decisions or justify the added complexity. We propose SIA, the first interpreter that exposes in real time how forecast- augmented DRL agents operate. SIA fuses Symbolic AI abstractions with per- KPI Knowledge Graphs to produce explanations, and includes a new Influence Score ( IS) metric. SIA achieves sub-millisecond speed, over 200× faster than existing EXplainable Artificial Intelligence (XAI) methods. We evaluate SIA on three diverse networking use cases, uncovering hidden issues, including temporal misalignment in forecast integration and reward-design biases that trigger counter-productive policies. These insights enable targeted fixes: a redesigned agent achieves a 9% higher average bitrate in video streaming, and SIA’s online Action-Refinement module improves RAN-slicing reward by 25% without retraining. By making anticipatory DRL transparent and tunable, SIA lowers the barrier to proactive control in next-generation mobile networks.