# Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

arXiv: https://arxiv.org/abs/2101.05317 · HTML: https://mlsanity.com/paper/2101.05317/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning](https://mlsanity.com/paper/1803.11347/) (2018)
- [Preparing for the Unknown: Learning a Universal Policy with Online System Identification](https://mlsanity.com/paper/1702.02453/) (2017)
- [Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm](https://mlsanity.com/paper/1710.11622/) (2017)
- [Learning Fast Adaptation with Meta Strategy Optimization](https://mlsanity.com/paper/1909.12995/) (2019)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.10597/) (2019)
- [Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control](https://mlsanity.com/paper/2006.12667/) (2020)
- [Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China](https://mlsanity.com/paper/1909.11711/) (2019)

## Built on by (most cited first, 7 of 7)

- [DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories](https://mlsanity.com/paper/2202.07176/) (2022)
- [Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery](https://mlsanity.com/paper/2111.14352/) (2021)
- [Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency](https://mlsanity.com/paper/2405.05521/) (2024)
- [Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/2212.02715/) (2022)
- [Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning](https://mlsanity.com/paper/2401.12235/) (2024)
- [Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights](https://mlsanity.com/paper/2410.19880/) (2024)
- [Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning](https://mlsanity.com/paper/2506.08893/) (2025)

## Abstract (arXiv)

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method and achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.