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• W3185807839 startingPage "7491" @default.
• W3185807839 abstract "Advancements in computational sciences have stimulated the use of an abundant amount of data in control and monitoring. Recent studies have reemphasized that the performance of the data-driven control significantly depends on the data quality. This quality is affected by uncertainties such as process and measurement noises. This study addresses a type of noise commonly seen in industry and shows how it degrades the performance of a deep reinforcement learning (RL) agent. Then, a novel filter is proposed to reduce the effect of this noise when it causes skewed probabilistic distributions in the reward functions. We demonstrate that the RL policy can be improved by using a constrained filter with a combination of the optimal filtering and RL concepts. The proposed algorithm is applied to a pilot-scale separation process that resembles an industrial separation vessel. The experimental results demonstrate that the proposed algorithm can improve the process operation efficiency." @default.
• W3185807839 created "2021-08-02" @default.
• W3185807839 creator A5017597087 @default.
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• W3185807839 date "2022-07-01" @default.
• W3185807839 modified "2023-09-24" @default.
• W3185807839 title "Reinforcement Learning With Constrained Uncertain Reward Function Through Particle Filtering" @default.
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• W3185807839 doi "https://doi.org/10.1109/tie.2021.3099234" @default.
• W3185807839 hasPublicationYear "2022" @default.
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