Reliable and accurate autonomous flow operation based on off-line trained reinforcement learning

Abstract

A RL agent trained offline for reliability and able to refine its policies during online operation is proposed. Results for three illustrative flow automation use cases show remarkable performance with extraordinary adaptability to changes.The research leading to these results has received funding from the Spanish MINECO TWINS project (TEC2017-90097-R) and by the ICREA institution.Peer ReviewedPostprint (published version

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