RL-STPA: Adapting System-Theoretic Hazard Analysis for Safety-Critical Reinforcement Learning

Researchers introduce RL-STPA, a framework adapting traditional hazard analysis methods to identify safety risks in reinforcement learning systems deployed in critical domains. The approach combines hierarchical task decomposition, perturbation testing, and iterative feedback loops to address RL's opacity and training-deployment misalignment.

MentionsRL-STPA · STPA · reinforcement learning

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