AI RESEARCH
RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning
arXiv CS.LG
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ArXi:2604.15201v1 Announce Type: new As reinforcement learning (RL) deployments expand into safety-critical domains, existing evaluation methods fail to systematically identify hazards arising from the black-box nature of neural network enabled policies and distributional shift between