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Research questionHow can reinforcement-learning controllers distinguish stochastic risk from out-of-distribution anomalies in distribution networks?Distribution-network operators must decide whether uncertain behavior reflects ordinary operational variability or an anomalous condition requiring safer control. The distinction is difficult because both types of behavior can affect the reliability of an RL control policy.
Machine Learning
Reinforcement Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty QuantificationThe source studies deep reinforcement-learning control with aleatoric and epistemic uncertainty estimates, using simulations to characterize operational risk, detect out-of-distribution behavior, and trigger fallback control during deployment. It does not provide evidence from physical grid deployment.research paper · Sep 3, 2026
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