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

TL;DR

SIA introduces a real-time symbolic interpretability method for forecast-augmented deep reinforcement learning in network control, enhancing transparency and enabling targeted improvements in network performance.

Contribution

SIA is the first interpreter to provide real-time explanations for forecast-augmented DRL agents in networking, combining symbolic AI and knowledge graphs for transparency.

Findings

SIA operates at sub-millisecond speed, over 200x faster than existing XAI methods.

Applying SIA revealed issues like temporal misalignment and reward biases in network agents.

Targeted fixes based on SIA insights improved network metrics such as bitrate and reward.

Abstract

Deep reinforcement learning (DRL) promises adaptive 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 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 metric. SIA achieves sub-millisecond speed, over 200x faster than existing 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.