Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

arXiv:2609.03308 · cs.LG, cs.MA, cs.SY, eess.SY · Submitted 2026-09-03 · Read on arXiv

cs.LG, cs.MA, cs.SY, eess.SY

Submitted: 2026-09-03

Updated: 2026-09-03

Terminology

• Actor — 21× in this paper · explained in 3 episodes
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Calibration — 12× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 8× in this paper · explained in 2 episodes
• Uncertainty Quantification — 8× in this paper · explained in 8 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Epistemic Uncertainty — 7× in this paper · explained in 10 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Action Space — 6× in this paper · explained in 1 episode
• Wasserstein Distance — 6× in this paper · explained in 4 episodes
• 2-Wasserstein Distance — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Monte Carlo dropout — 5× in this paper · explained in 2 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• AUROC — 3× in this paper · explained in 2 episodes
• Adversarial Examples — 3× in this paper · explained in 2 episodes
• Aleatoric Uncertainty — 3× in this paper · explained in 5 episodes
• Distribution Shift — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• National Renewable Energy Laboratory — 1× in this paper
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Proximal Policy Optimization Algorithms

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