Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-CriticDownload PDF

Sep 28, 2020 (edited Mar 18, 2021)ICLR 2021 SpotlightReaders: Everyone
  • Keywords: power grid management, deep reinforcement learning, graph neural network
  • Abstract: Safe and reliable electricity transmission in power grids is crucial for modern society. It is thus quite natural that there has been a growing interest in the automatic management of power grids, exemplified by the Learning to Run a Power Network Challenge (L2RPN), modeling the problem as a reinforcement learning (RL) task. However, it is highly challenging to manage a real-world scale power grid, mostly due to the massive scale of its state and action space. In this paper, we present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by RL, adopting the hierarchical policy together with the afterstate representation. Our agent ranked first in the latest challenge (L2RPN WCCI 2020), being able to avoid disastrous situations while maintaining the highest level of operational efficiency in every test scenarios. This paper provides a formal description of the algorithmic aspect of our approach, as well as further experimental studies on diverse power grids.
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  • One-sentence Summary: We present an off-policy actor-critic approach that effectively tackles the unique challenges in power grid management by reinforcement learning, adopting the hierarchical policy together with the afterstate representation.
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