Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

被引：0

作者：

Chenliang Liu ^{[1
]}

Yalin Wang ^{[2
,1
]}

Chunhua Yang ^{[2
,1
]}

Weihua Gui ^{[1
]}

机构：

[1] the School of Automation, Central South University

[2] IEEE

来源：

IEEE/CAA Journal of Automatica Sinica | 2024年 / 11卷 / 01期

基金：

中央高校基本科研业务费专项资金资助; 中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio,

引用

页码：252 / 254

页数：3

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