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Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections.
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise).
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