@inbook{b139f23fbf6c403eb225647f4748be6c,
title = "Using Reinforcement Learning in a Game-like Setup for Automated Process Synthesis without Prior Process Knowledge",
abstract = "The present work uses reinforcement learning (RL) for automated flowsheet synthesis. The task of synthesizing a flowsheet is reformulated into a two-player game, in which an agent learns by self-play without prior knowledge. The hierarchical RL scheme developed in our previous work (G{\"o}ttl et al., 2021b) is coupled with an improved training process. The training process is analyzed in detail using the synthesis of ethyl tert-butyl ether (ETBE) as an example. This analysis uncovers how the agent's evolution is driven by the two-player setup.",
keywords = "Artificial Intelligence, Automated Process Synthesis, Flowsheet Synthesis, Machine Learning, Reinforcement Learning",
author = "Quirin G{\"o}ttl and Grimm, {Dominik G.} and Jakob Burger",
note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = jan,
doi = "10.1016/B978-0-323-85159-6.50259-1",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "1555--1560",
booktitle = "Computer Aided Chemical Engineering",
}