{"id":94219,"date":"2021-04-15T12:00:00","date_gmt":"2021-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=94219"},"modified":"2024-03-27T14:30:04","modified_gmt":"2024-03-27T13:30:04","slug":"potentials-of-reinforcement-learning-for-production","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/potentials-of-reinforcement-learning-for-production\/","title":{"rendered":"Potentials of Reinforcement Learning for Production"},"content":{"rendered":"\n
\n
Bibliography <\/h2>\n[1] D.Silver,J.Schrittwieser,K.Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel und D. Hassabis, \u201eMastering the game of Go without human knokwledge,\u201c Nature 550, Oktober 2017. \n[2] A. Borghesi, A. Bartolini, M. Lombardi, M. Milano und L. Benini,\u201eAnomaly Detection Using Autoencoders in High Performance Computing Systems,\u201c in The Thirty-First AAAI Conference on Innovative Applications of Artificial Intelligence, 2019. \n[3] R.S.SuttonundA.G.Barto,Reinforcement learning: An introduction, MIT press, 2018. \n[4] T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel und S. Levine, \u201eSoft Actor-Critic Algorithms and Applications,\u201c 2018. \n[5] J.Schulman,F.Wolski,P.Dhariwal, A. Radford und O. Klimov, \u201eProximal Policy Optimization Algorithms,\u201c arXiv Preprint, 2017. \n[6] X.Peng,W.Andrychowicz,W. Zaremba und P. Abbeel, \u201eSim-to-Real Transfer of Robotic Control with Dynamics Randomization,\u201c 2017. \n[7] Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff und D. Fox, \u201eClosing the Sim-To-Real Loop: Adapting Simulation, Randomization with Real World Experience,\u201c 2018. \n[8] R. Dittmar und B.-M. Pfeiffer, Modellbasierte pr\u00e4diktive Regelung: Eine Einf\u00fchrung f\u00fcr Ingenieure, Walter de Gruyter, 2009. \n[9] B. Waschneck, Autonome Entscheidungsfindung in der Produktionssteuerung komplexer Werkstattfertigungen, Stuttgart: 2020. \n[10]T. Altenm\u00fcller, T. St\u00fcker, B. Waschneck, A. Kuhnle und G. Lanza, \u201eReinforcement learning for an intelligent and autonomous production control of complex job-shops under time constraints,\u201c Production Engineering 14, 2020. \n[11] D. Silver und J. Veness, \u201eMonte-Carlo Planning in Large POMDPs,\u201c (NIPS) Advances in Neural Information Processing Systems, 2010. \n[12] M. El-Shamouty, K. Kleeberger, A. L\u00e4mmle und M. Huber, \u201eSimulation-driven machine learning for robotics and automation,\u201c tm -Technisches Messen, pp. 673-684, August 2019.<\/div>\n