Architecture for Digital Twin-Based Reinforcement Learning Optimization of Cyber-Physical Systems-Reference-Cited by-同舟云学术

Architecture for Digital Twin-Based Reinforcement Learning Optimization of Cyber-Physical Systems

Published:2024 Issue: Volume: Page:257-271
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

Modrakowski Elias^ORCID,Braun Niklas,Hajnorouzi Mehrnoush^ORCID,Eich Andreas^ORCID,Javaheri Narges,Doornbos Richard,Moritz Sebastian^ORCID,Bikker Jan-Willem,van Beek Rutger

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-66326-0_16

Reference33 articles.

1. Abdolmaleki, A., Springenberg, J.T., Tassa, Y., Munos, R., Heess, N., Riedmiller, M.: Maximum a posteriori policy optimisation (2018). https://doi.org/10.48550/arXiv.1806.06920

2. Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y.: Digital twin as a service (DTAAS) in industry 4.0: An architecture reference model. Adv. Eng. Inform. 47, 101225 (2021). https://doi.org/10.1016/j.aei.2020.101225

3. Alexopoulos, K., Nikolakis, N., Chryssolouris, G.: Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int. J. Comput. Integr. Manuf. 33(5), 429–439 (2020). https://doi.org/10.1080/0951192X.2020.1747642

4. Amiranashvili, A., Argus, M., Hermann, L., Burgard, W., Brox, T.: Pre-training of deep RL agents for improved learning under domain randomization. eprint arXiv:2104.14386 (2021). https://doi.org/10.48550/arXiv.2104.14386

5. Barricelli, B.R., Casiraghi, E., Fogli, D.: A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671 (2019). https://doi.org/10.1109/ACCESS.2019.2953499