# Distribution Network Self-Healing Decision-Making

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.