mlsanityData to 7 Oct 2026

Rethink AI-based Power Grid Control: Diving Into Algorithm Design

arXiv 2012.13026 ↗First posted 23 Dec 2020cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

Abstract (arXiv)

Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process. The performance results demonstrate that theproposed approach has strong generalization ability with much less training time.The agent trained by imitation learning is effective and robust to solve voltagecontrol problem and outperforms the former RL agents.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 23 Dec 2020.

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