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

• URL: http://arxiv.org/abs/2601.10973v1
• Date: Fri, 16 Jan 2026 03:36:07 GMT
• Title: Toward Adaptive Grid Resilience: A Gradient-Free Meta-RL Framework for Critical Load Restoration
• Abstract summary: Reinforcement learning can optimize sequential decisions under uncertainty.<n>Standard RL often generalizes poorly and requires extensive retraining for new outage configurations or generation patterns.<n>We propose a meta-guided gradient-free RL framework that learns a transferable initialization from historical outage experiences.
• Score: 6.573424608566227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Reinforcement Learning via Value Gradient Flow [32.59594977900539]
  We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution is essential to prevent value over-optimization.<n>We propose Value Gradient Flow (VGF), a scalable new paradigm for behavior-regularized RL.<n>VGF casts behavior-regularized RL as an optimal transport problem that maps the reference distribution to the value-induced optimal policy distribution.
  arXiv  Detail & Related papers  (2026-04-15T17:12:56Z)
• Tackling Length Inflation Without Trade-offs: Group Relative Reward Rescaling for Reinforcement Learning [74.5532558466687]
  Group Relative Reward Rescaling (GR$3$) is a general, continuous, and reward-dependent gating mechanism.<n> GR$3$maintains training dynamics and downstream performance comparable to standard GRPO.<n>It significantly mitigates length inflation, outperforming state-of-the-art length-regularized baselines.
  arXiv  Detail & Related papers  (2026-03-11T08:41:34Z)
• Offline Meta-Reinforcement Learning with Flow-Based Task Inference and Adaptive Correction of Feature Overgeneralization [12.107082786676907]
  offline meta-reinforcement learning (OMRL) combines the strengths of learning from diverse datasets in offline RL with the adaptability to new tasks of meta-RL.<n>Existing research indicates that the generalization of the $Q$ network affects the extrapolation error in offline RL.<n>We propose FLORA, which identifies OOD samples by modeling feature distributions and estimating their uncertainties.
  arXiv  Detail & Related papers  (2026-01-12T03:16:07Z)
• Sample-Efficient Neurosymbolic Deep Reinforcement Learning [49.60927398960061]
  We propose a neuro-symbolic Deep RL approach that integrates background symbolic knowledge to improve sample efficiency.<n>Online reasoning is performed to guide the training process through two mechanisms.<n>We show improved performance over a state-of-the-art reward machine baseline.
  arXiv  Detail & Related papers  (2026-01-06T09:28:53Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward [93.04811239892852]
  Reinforcement Learning (RL) has recently been incorporated into diffusion models.<n>In this paper, we investigate how to effectively integrate RL into diffusion-based restoration models.
  arXiv  Detail & Related papers  (2025-11-03T14:57:57Z)
• Group-Relative REINFORCE Is Secretly an Off-Policy Algorithm: Demystifying Some Myths About GRPO and Its Friends [64.71326476563213]
  Off-policy reinforcement learning for large language models (LLMs) is attracting growing interest.<n>We present a first-principles derivation for grouprelative REINFORCE without assuming a specific training data distribution.<n>This perspective yields two general principles for adapting REINFORCE to off-policy settings.
  arXiv  Detail & Related papers  (2025-09-29T02:34:54Z)
• Hybrid and Unitary Fine-Tuning of Large Language Models: Methods and Benchmarking under Resource Constraints [0.0]
  Fine-tuning large language models (LLMs) remains a computational bottleneck due to their scale and memory demands.<n>This paper presents a comprehensive evaluation of parameter-efficient fine-tuning (PEFT) techniques, including LoRA, BOFT, LoRA-GA, and uRNN.<n>By computing per-layer adaptive updates guided by gradient norms, the hybrid method achieves superior convergence efficiency and generalization across diverse tasks.
  arXiv  Detail & Related papers  (2025-07-24T04:00:02Z)
• RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget [28.53294084812961]
  Real-time machine learning algorithms are often faced with the challenge of adapting models to concept drift.<n>Existing solutions often depend on drift-detection methods that produce high computational overhead for resource-constrained environments.<n>We propose RCCDA: a dynamic model update policy that optimize ML training dynamics while ensuring compliance to predefined resource constraints.
  arXiv  Detail & Related papers  (2025-05-30T02:49:42Z)
• Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies [12.599164162404994]
  This study introduces an automatic reward generation paradigm by leveraging the in-context learning and reasoning capabilities of Large Language Models (LLMs)<n>To evaluate the feasibility of the proposed LLM-enhanced RL paradigm, it is applied to extensive bus holding control scenarios that vary in the number of bus lines, stops, and passenger demand.
  arXiv  Detail & Related papers  (2024-10-14T07:10:16Z)
• Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers [0.37109226820205005]
  We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p using Reinforcement Learning (RL) We develop an offline training approach, decoupled from the main solver, which shows minimal overcost when performing simulations. We derive an inexpensive RL-based error estimation approach that enables the quantification of local discretization errors.
  arXiv  Detail & Related papers  (2024-07-26T17:55:23Z)
• REBEL: Reinforcement Learning via Regressing Relative Rewards [59.68420022466047]
  We propose REBEL, a minimalist RL algorithm for the era of generative models.<n>In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL.<n>We find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO.
  arXiv  Detail & Related papers  (2024-04-25T17:20:45Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
  We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation. The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
  arXiv  Detail & Related papers  (2021-03-01T22:55:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.