%0 Conference Paper %1 jaensch2018reinforcement %A Jaensch, Florian %A Csiszar, Akos %A Kienzlen, Annika %A Verl, Alexander %B 2018 First International Conference on Artificial Intelligence for Industries (AI4I) %C Piscataway, NJ %D 2018 %I IEEE %K %P 77-80 %R 10.1109/AI4I.2018.8665712 %T Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation %X In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy. %@ 978-1-5386-9209-7 and 978-1-5386-9463-3

@inproceedings{jaensch2018reinforcement, abstract = {In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.}, added-at = {2023-08-31T15:03:03.000+0200}, address = {Piscataway, NJ}, author = {Jaensch, Florian and Csiszar, Akos and Kienzlen, Annika and Verl, Alexander}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c9324e84efe8aab59d8f55f1b95a8d99/puma-wartung}, booktitle = {2018 First International Conference on Artificial Intelligence for Industries (AI4I)}, doi = {10.1109/AI4I.2018.8665712}, eventdate = {2018-09-26/2018-09-28}, eventtitle = {2018 First International Conference on Artificial Intelligence for Industries (AI4I)}, interhash = {359b8b3bc1b319e37551a5aff6196ff4}, intrahash = {c9324e84efe8aab59d8f55f1b95a8d99}, isbn = {{978-1-5386-9209-7} and {978-1-5386-9463-3}}, keywords = {}, language = {eng}, pages = {77-80}, publisher = {IEEE}, timestamp = {2023-08-31T13:03:03.000+0200}, title = {Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation}, venue = {Laguna Hills, CA, USA}, year = 2018 }