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Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Ensuring safety in reinforcement learning under nonstationarity requires anticipating changes in risk before they lead to unsafe behavior. Existing approaches typically rely on safety constraints defined at design time or updated reactively during execution, assuming that such constraints remain valid over time. However, in nonstationary environments with evolving contexts and changing driving layouts, these assumptions may fail. We propose a framework for proactive safety constraint generation based on context forecasting. The approach infers latent environmental context from observations, pr

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