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

作　　者：Chenliang Liu Yalin Wang Chunhua Yang Weihua Gui 

机构地区：[1]the School of Automation,Central South University,Changsha 410083,China [2]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第1期252-254,共3页自动化学报（英文版）

基　　金：supported by the National Key Research and Development Program of China (2020YFB1713800);the National Natural Science Foundation of China (92267205);the Hunan Provincial Innovation Foundation for Postgraduate (CX2022 0267);the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0181)。

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.

关 键 词：processes MODAL ADJUST 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]