Document Type

Conference Paper



Publication Details

Abbas, A.N., Chasparis, G.C. & Kelleher, J.D. Deep Residual Policy Reinforcement Learning as a Corrective Term in Process Control for Alarm Reduction: A Preliminary Report. Proceedings of the 32nd European,Safety and Reliability Conference (ESREL 2022), 28th August – 1st September 2022, Dublin, Ireland

doi: 10.3850/981-973-0000-00-0 output


Conventional process controllers (such as proportional integral derivative controllers and model predictive controllers) are simple and effective once they have been calibrated for a given system. However, it is difficult and costly to re-tune these controllers if the system deviates from its normal conditions and starts to deteriorate. Recently, reinforcement learning has shown a significant improvement in learning process control policies through direct interaction with a system, without the need of a process model or the system characteristics, as it learns the optimal control by interacting with the environment directly. However, developing such a black-box system is a challenge when the system is complex and it may not be possible to capture the complete dynamics of the system with just a single reinforcement learning agent. Therefore, in this paper, we propose a simple architecture that does not replace the conventional proportional integral derivative controllers but instead augments the control input to the system with a reinforcement learning agent. The agent adds a correction factor to the output provided by the conventional controller to maintain optimal process control even when the system is not operating under its normal condition.

DOI output


Science Foundation Ireland (SFI) Research Centres Program (Grant No. 13/RC/2106 P2).

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Creative Commons Attribution-Share Alike 4.0 International License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.