Shared communication for coordinated large-scale reinforcement learning control

Shared communication for coordinated large-scale reinforcement learning control

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them mathematically. In contrast to classical controllers, DRL alleviates the need for constant parameter tuning, tedious design of control laws, and re-identification procedures...

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Bibliographic Details
Main Authors: Nicolas Bougie, Takashi Onishi, Yoshimasa Tsuruoka
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:SICE Journal of Control, Measurement, and System Integration
Subjects:
deep reinforcement learning
coordinated control
large-scale reinforcement learning
chemical process control
reinforcement learning for process control
Online Access:http://dx.doi.org/10.1080/18824889.2023.2174647

Internet

http://dx.doi.org/10.1080/18824889.2023.2174647

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