Interpretable Attention-based Multi-agent PPO For Latency Spike Resolution In 6G RAN Slicing

Abstract

Sixth-generation (6G) radio access networks (RANs) must enforce strict service-level agreements (SLAs) for heterogeneous slices, yet sudden latency spikes remain difficult to diagnose and resolve with conventional deep reinforcement learning (DRL) or explainable RL (XRL). We propose *Attention-Enhanced Multi-Agent Proximal Policy Optimization (AE-MAPPO)*, which integrates six specialized attention mechanisms into multi-agent slice control and surfaces them as zero-cost, faithful explanations. The framework operates across O-RAN timescales with a three-phase strategy: predictive, reactive, and inter-slice optimization. A URLLC case study shows AE-MAPPO resolves a latency spike in \(18\)ms, restores latency to \(0.98\)ms with \(99.9999%\) reliability, and reduces troubleshooting time by \(93%\) while maintaining eMBB and mMTC continuity. These results confirm AE-MAPPO's ability to combine SLA compliance with inherent interpretability, enabling trustworthy and real-time automation for 6

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