Aleksandra Dmitruka
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126
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ML×1AI×1
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2026
Interpretable Policy Distillation for Power Grid Topology Control
This paper demonstrates that a complex deep reinforcement learning policy for power grid control can be successfully distilled into a lightweight, auditable decision tree and random forest surrogate that maintains or exceeds the original performance.
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