Architecture and technical approach for DT-based AI-training: state of the art
report
This report describes the state of the art in reinforcement learning and digital twin-based learning, and the first ideas on their application in the ASIMOV use cases. It will be the foundation for further work on researching these techniques in the ASIMOV use cases, which will enable expansion of the knowledge in these fields for general application in the high-tech industry. A low-threshold introduction to reinforcement learning and Q-learning is followed by an extensive and well-structured literature overview. Finally, the initial ideas about which techniques and approaches to use and how to apply them in the use cases are described in the last part of this report.
TNO Identifier
1006493
Publisher
Asimov
Collation
40 p.
Place of publication
Eindhoven