Hierarchical Reinforcement Learning with Runtime Safety Shielding for Power Grid Operation

ArXi:2604.14032v1 Announce Type: new Reinforcement learning has shown promise for automating power-grid operation tasks such as topology control and congestion management. However, its deployment in real-world power systems remains limited by strict safety requirements, brittleness under rare disturbances, and poor generalization to unseen grid topologies. In safety-critical infrastructure, catastrophic failures cannot be tolerated, and learning-based controllers must operate within hard physical constraints.