Shared communication for coordinated large-scale reinforcement learning control-Reference-Cited by-同舟云学术

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Shared communication for coordinated large-scale reinforcement learning control

Published:2023-02-26 Issue:1 Volume:16 Page:27-37
ISSN:1882-4889
Container-title:SICE Journal of Control, Measurement, and System Integration
language:en
Short-container-title:SICE Journal of Control, Measurement, and System Integration

Author:

Bougie Nicolas¹,Onishi Takashi¹²,Tsuruoka Yoshimasa¹³

Affiliation:

1. NEC-AIST AI Cooperative Research Laboratory, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

2. Data Science Research Laboratories, NEC Corporation, Kanagawa, Japan

3. Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan

Publisher

Informa UK Limited

Subject

General Medicine

Link

https://www.tandfonline.com/doi/pdf/10.1080/18824889.2023.2174647

Reference22 articles.

1. Spielberg S Tulsyan A Lawrence NP et al. Deep reinforcement learning for process control: a primer for beginners. arXiv preprint arXiv:200405490. 2020.

2. Scalable reinforcement learning for plant-wide control of vinyl acetate monomer process

3. Cui Y Zhu L Fujisaki M et al. Factorial Kernel dynamic policy programming for vinyl acetate monomer plant model control. In: Proceedings of the IEEE International Conference on Automation Science and Engineering; 2018. p. 304–309.

4. Computing operation procedures for chemical plants using whole-plant simulation models

5. Mori T Kubosawa S Onishi T et al. Improving the gross profit of a vinyl acetate monomer plant by deep reinforcement learning. In: Annual Conference of the Society of Instrument and Control Engineers of Japan; 2020.

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