Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Qing, Yunpeng"'
Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize the voltage levels to ensure efficient and reliable operation of power systems. With the increasing integration of distributed energy resources, recent efforts h
Externí odkaz:
http://arxiv.org/abs/2406.17818
Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution pr
Externí odkaz:
http://arxiv.org/abs/2403.07262
Autor:
Chen, Kaixuan, Luo, Wei, Liu, Shunyu, Wei, Yaoquan, Zhou, Yihe, Qing, Yunpeng, Zhang, Quan, Song, Jie, Song, Mingli
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, o
Externí odkaz:
http://arxiv.org/abs/2401.02771
Autor:
Liu, Shunyu, Qing, Yunpeng, Xu, Shuqi, Wu, Hongyan, Zhang, Jiangtao, Cong, Jingyuan, Chen, Tianhao, Liu, Yunfu, Song, Mingli
Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function from expert demonstrations to facilitate policy learning, and has demonstrated its remarkable success in imitation learning. To promote expert-like behavior, existing IRL me
Externí odkaz:
http://arxiv.org/abs/2306.08232
Autor:
Zhou, Yihe, Liu, Shunyu, Qing, Yunpeng, Chen, Kaixuan, Zheng, Tongya, Huang, Yanhao, Song, Jie, Song, Mingli
Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use additional global state information to guide training in a centralize
Externí odkaz:
http://arxiv.org/abs/2305.17352
Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of deep learning, Deep RL (DRL) has witnessed great success over a wide sp
Externí odkaz:
http://arxiv.org/abs/2211.06665