{"version":"1.0","generated_at":"2026-10-08T00:28:05.014277","id":95,"slug":"hierarchical-reinforcement-learning-with-runtime-safety-shielding-for-power-grid-operation","title":"论文速读：Hierarchical Reinforcement Learning with Runtime Safety Shielding for，解读最新研","summary":"本文提出一种安全约束的分层控制框架，用于电网运营，通过高级RL策略和运行时安全防护罩实现长期决策与安全执行分离，在Grid2Op基准测试中表现出更好的泛化性和安全性。","abstract":"论文速读：Hierarchical Reinforcement Learning with Runtime Safety Shielding for，解读最新研 本文提出一种安全约束的分层控制框架，用于电网运营，通过高级RL策略和运行时安全防护罩实现长期决策与安全执行分离，在Grid2Op基准测试中表现出更好的泛化性和安全性。 提出分层RL+安全防护罩框架，分离长期决策与实时安全。 在Grid2Op上实现更长存活期和更低峰值负载。 零样本泛化到未见过的大型电网。 安全性通过架构设计实现，而非奖励工程。 arXiv:2604.14032v1 公告类型：新 摘要：强化学习在自动化电网运营任务（例如拓扑控制和拥塞管理）方面显示出了前景。然而，其在现实世界电力系统中的部署仍然受到严格的安全要求、罕见干扰下的脆弱性以及对不可见的电网拓扑的泛化性差等限制。在安全关键型基础设施中，灾难性故障是不能容忍的，基于学习的控制器必须在严格的物理约束下运行。本文提出了一种用于电网运行的安全约束分层控制框架，该框架明确地将长期决策与实时可行性执行脱钩。高级强化学习策略提出抽象控制操作，而确定性运行时安全防护罩则使用快进模拟过滤不安全操作。安全性是作为运行时不变性强制执行的，与策略质量或培训分布无关。所提出的框架在 Grid2Op 基准套件上进行了标称条件下的评估、强制线路中断压力测试以及无需重新训练的 ICAPS 2021 大型输电电网上的零次部署。结果表明，平面强化学习策略在压力下很脆弱，而仅考虑安全的方法则过于保守。相比之下，所提出的分层和安全意识方法实现了更长的事件生存期、更低的峰值线负载以及对…","access_level":"public","access_label":"公开","access_mode":"full","is_preview":false,"canonical_url":"https://opc.beizhux.com/content/95/hierarchical-reinforcement-learning-with-runtime-safety-shielding-for-power-grid-operation","html_url":"https://opc.beizhux.com/content/95/hierarchical-reinforcement-learning-with-runtime-safety-shielding-for-power-grid-operation","json_url":"https://opc.beizhux.com/content/95/hierarchical-reinforcement-learning-with-runtime-safety-shielding-for-power-grid-operation.json","published_at":"2026-04-17T23:59:14","updated_at":"2026-10-06T22:56:05","category":{"slug":"hotspots","name":"全球热点解读"},"source":{"site":"arxiv.org","author":"arXiv cs.AI","url":"https://arxiv.org/abs/2604.14032"},"tags":["AI","AI部署","arXiv 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