Cooperation Pattern Exploration for Multi-Agent Reinforcement Learning

Zhao, Chenran; Peng, Kecheng; Cao, Xiaoqun

doi:10.1109/mlccim55934.2022.00077

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The results listed in table 2 below are from experiments in our previous work [24] . Among the tested models, ResNet and ViT perform well on both balanced and imbalanced dataset, moreover, given the mean gap of 4.27% between the two models on 'LARGE' dataset and relatively minus gap of -1.47% on 'MIDDLE', we choose to implement ResNet into the detection module.…”

Section: Acc = Tp+tn Tp+tn+fp+fnmentioning

confidence: 99%

Design and Implementation of Power Big Data Platform

Zhao,

Li,

Zhao

et al. 2023

J. Phys.: Conf. Ser.

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In recent years, with the advancement of the national construction of smart grid and the development of power grid enterprise integration system, the traditional power data platform has some defect, such as insufficient scalability, repeated implementation of two sets of logic of off-line data warehouse and real-time data warehouse, highly correlated storage of data warehouse during computation, and difficulty in platform migration, which restrict the deeper mining and analysis of power big data. Therefore, this paper adopts a technical architecture incorporating distributed calculating, micro-service, flow-batch integration and lake-warehouse integration, combining the current popular Web front-end and back-end technologies such as Vue, SpringCloud and Flask, and utilizing big data components such as Hadoop, Flink, Hudi and Kafka as well as Docker containerization technology. We explore and build a power grid system operation oriented big data platform that assembles real-time computing, multi-source heterogeneous data storage, and develop APIs for authority management, information management, data analysis and visualization, machine learning and other data supporting services that assist power enterprises in managing electrical equipment and users’ power consumption.With the help of this platform, power enterprises can intelligently track the equipment status, view the offline and real-time visual charts to analyze the power consumption by users, so as to have better basis in the power dispatching, maintenance and scheme adjustment of electrical market.

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Section: Acc = Tp+tn Tp+tn+fp+fnmentioning

confidence: 99%