# Toward Adaptive Grid Resilience: A Gradient-Free Meta-RL Framework for Critical Load Restoration

arXiv: https://arxiv.org/abs/2601.10973 · HTML: https://mlsanity.com/paper/2601.10973/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Non-stationary Stochastic Optimization](https://mlsanity.com/paper/1307.5449/) (2013)
- [Dynamic Regret of Convex and Smooth Functions](https://mlsanity.com/paper/2007.03479/) (2020)
- [Temporal Variability in Implicit Online Learning](https://mlsanity.com/paper/2006.07503/) (2020)
- [A CMDP-within-online framework for Meta-Safe Reinforcement Learning](https://mlsanity.com/paper/2405.16601/) (2024)
- [Convex Relaxations and Linear Approximation for Optimal Power Flow in Multiphase Radial Networks](https://mlsanity.com/paper/1406.3054/) (2014)
- [Critical Load Restoration using Distributed Energy Resources for Resilient Power Distribution System](https://mlsanity.com/paper/1912.04535/) (2019)
- [Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration](https://mlsanity.com/paper/2203.04166/) (2022)
- [Risk-limiting Load Restoration for Resilience Enhancement with Intermittent Energy Resources](https://mlsanity.com/paper/1704.05411/) (2017)
- [Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems](https://mlsanity.com/paper/1810.06907/) (2018)
- [Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration](https://mlsanity.com/paper/1905.06599/) (2019)

## Abstract (arXiv)

Restoring critical loads after extreme events demands adaptive control to maintain distribution-grid resilience, yet uncertainty in renewable generation, limited dispatchable resources, and nonlinear dynamics make effective restoration difficult. Reinforcement learning (RL) can optimize sequential decisions under uncertainty, but standard RL often generalizes poorly and requires extensive retraining for new outage configurations or generation patterns. We propose a meta-guided gradient-free RL (MGF-RL) framework that learns a transferable initialization from historical outage experiences and rapidly adapts to unseen scenarios with minimal task-specific tuning. MGF-RL couples first-order meta-learning with evolutionary strategies, enabling scalable policy search without gradient computation while accommodating nonlinear, constrained distribution-system dynamics. Experiments on IEEE 13-bus and IEEE 123-bus test systems show that MGF-RL outperforms standard RL, MAML-based meta-RL, and model predictive control across reliability, restoration speed, and adaptation efficiency under renewable forecast errors. MGF-RL generalizes to unseen outages and renewable patterns while requiring substantially fewer fine-tuning episodes than conventional RL. We also provide sublinear regret bounds that relate adaptation efficiency to task similarity and environmental variation, supporting the empirical gains and motivating MGF-RL for real-time load restoration in renewable-rich distribution grids.