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Daniel McClement
Originally from Calgary, Daniel moved to Vancouver to pursue a bachelor's degree in engineering at the University of British Columbia. After being allured by the scenic hikes and beaches around the city as well as the interesting research being done by the DAIS Lab, he chose to stay for his master's degree. He is now studying reinforcement learning for use in control systems. Outside of his studies, Daniel enjoys skiing at Cypress Mountain in the winter and hiking around Vancouver in the summer.
📚 Program/Degree
GROUP ALUMNI (2022) |
MASc
| Started 2020
📝 Research
Deep Reinforcement Learning Algorithms for Maintenance-free Control
👥 Also supervised by
DAIS Lab Publications
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Conference Proceedings
Keynote Presentation
A Meta-Reinforcement Learning Approach to Process Control
In Proceedings of the 16th IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM 2021). 2021 [PDF] [Slides] [Video] [arXiv] -
Journal Paper
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Control Engineering Practice. 2021 [PDF] -
Conference Proceedings
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
In Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes (AdCONIP). 2022 [PDF] [Video] -
Journal Paper
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Journal of Process Control. 2022 [PDF] -
Patent
Process controller with meta-reinforcement learning
U.S. Patent Application No. 17/653,175.. 2022
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