# Reinforcement Learning for Resilient Power Grids

arXiv: https://arxiv.org/abs/2212.04069 · HTML: https://mlsanity.com/paper/2212.04069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Building Energy Load Forecasting using Deep Neural Networks](https://mlsanity.com/paper/1610.09460/) (2016)
- [Harnessing Structures for Value-Based Planning and Reinforcement Learning](https://mlsanity.com/paper/1909.12255/) (2019)
- [Low Rank Regularization: A Review](https://mlsanity.com/paper/1808.04521/) (2018)
- [Value function approximation via low-rank models](https://mlsanity.com/paper/1509.00061/) (2015)

## Built on by (most cited first, 1 of 1)

- [A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response](https://mlsanity.com/paper/2410.04518/) (2024)

## Abstract (arXiv)

Traditional power grid systems have become obsolete under more frequent and extreme natural disasters. Reinforcement learning (RL) has been a promising solution for resilience given its successful history of power grid control. However, most power grid simulators and RL interfaces do not support simulation of power grid under large-scale blackouts or when the network is divided into sub-networks. In this study, we proposed an updated power grid simulator built on Grid2Op, an existing simulator and RL interface, and experimented on limiting the action and observation spaces of Grid2Op. By testing with DDQN and SliceRDQN algorithms, we found that reduced action spaces significantly improve training performance and efficiency. In addition, we investigated a low-rank neural network regularization method for deep Q-learning, one of the most widely used RL algorithms, in this power grid control scenario. As a result, the experiment demonstrated that in the power grid simulation environment, adopting this method will significantly increase the performance of RL agents.