ConformalSafe-Routing: Uncertainty-Aware Safe Deep Reinforcement Learning for Adaptive Routing Convergence
Adaptive routing controllers based on deep reinforcement learning can shorten recovery after failures, but a policy optimized only for expected reward may select timer and damping configurations whose control overhead or route oscillation risk is poorly estimated under rare or shifted conditions. This paper presents ConformalSafe-Routing, a runtime safety layer for adaptive routing convergence. A dueling double deep Q-network ranks bounded routing profiles, while an independently trained risk model estimates one-step operational cost from topology, failure, load, and convergence-state features. Split conformal calibration converts point predictions into finite-sample upper bounds. A horizon-aware allocation uses a per-decision miscoverage budget of 0.0125 for an eight-step episode, and the shield selects the highest-value action whose upper bound satisfies the safety envelope; a balanced static profile is used when the certified set is empty. Experiments use four real telecom topologies from the Internet Topology Zoo and a fully released topology-driven event simulator. Across 300 paired episodes per condition, the proposed method reduces episode-level safety violations from 56.7% to 4.3% in-domain and from 64.0% to 2.0% under high-load distribution shift relative to unshielded DQN. It also reduces route flaps by 73.0% and 76.6%, respectively. Compared with a balanced static profile, it shortens mean convergence by 22.5% in-domain and 9.9% under shift while maintaining low violation rates. The results support conformal shielding as a practical mechanism for exposing and controlling the safety-speed trade-off in learning-based routing, while also identifying the limits of guarantees under topology and load shift.