arXiv:2505.09920

Offline Reinforcement Learning for Microgrid Voltage Regulation

Shan Yang, Yongli Zhu

제출 2025-05-15

원문 초록

This paper presents a study on using different offline reinforcement learning algorithms for microgrid voltage regulation with solar power penetration. When environment interaction is unviable due to technical or safety reasons, the proposed approach can still obtain an applicable model through offline-style training on a previously collected dataset, lowering the negative impact of lacking online environment interactions. Experiment results on the IEEE 33-bus system demonstrate the feasibility and effectiveness of the proposed approach on different offline datasets, including the one with merely low-quality experience.

함께 살펴볼 논문

공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.

arXiv:2505.22442v4cs.LG공통 태그 reinforcement-learning, Reinforcement Learning공식 코드 · Papers with Code

Fully Offline Reinforcement Learning

토론

이 논문과 연결된 포럼 글이 아직 없습니다.