Nathan Lawrence

Nathan Lawrence

Nathan received his PhD in mathematics from UBC in 2023. Prior to moving to Vancouver, he earned his Bachelor's and Master's degrees in mathematics at Portland State University. He is interested in the interplay between reinforcement learning and control. More specifically, his work aims to develop actionable methods based on deep reinforcement learning for maintenance-free PID control and MPC of industrial processes. Outside of research, he enjoys boardgames and ice skating.

📚 Program/Degree
Postdoc | Started 2023
📝 Research
Deep Reinforcement Learning for Maintenance-free Control
👥 Also supervised by
📨 Contact

DAIS Lab Publications

  1. Journal Paper Top 10% Most Downloaded
    Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control
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    AIChE Journal. 2019 [PDF]
  2. Conference Proceedings NeurIPS Spotlight
    Almost Surely Stable Deep Dynamics
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    In Proceedings of Advances in Neural Information Processing Systems 33 (NeurIPS 2020). 2020 [PDF] [Code] [Poster] [Video] [arXiv]
  3. Conference Proceedings
    Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
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    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Video] [arXiv]
  4. Conference Proceedings
    Reinforcement Learning based Design of Linear Fixed Structure Controllers
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    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Video] [arXiv]
  5. Conference Proceedings
    Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
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    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Supplementary Info]
  6. Conference Proceedings Keynote Presentation
    A Meta-Reinforcement Learning Approach to Process Control
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    In Proceedings of IFAC International Symposium on Advanced Control of Chemical Processes (ADCHEM, To Appear). 2021 [PDF] [Slides] [Video] [arXiv]
  7. Journal Paper
    Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
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    Control Engineering Practice. 2021 [PDF]
  8. Conference Proceedings
    Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
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    In Proceedings of the 7th International Symposium on Advanced Control of Industrial Processes (AdCONIP). 2022 [PDF] [Video]
  9. Journal Paper
    Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
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    Journal of Process Control. 2022 [PDF]
  10. Conference Proceedings
    A modular framework for stabilizing deep reinforcement learning control
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    In Proceedings of the 22nd IFAC World Congress (To Appear). 2023 [PDF] [Slides] [Video] [arXiv]
  11. Conference Proceedings
    Reinforcement Learning with Partial Parametric Model Knowledge
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    In Proceedings of the 22nd IFAC World Congress (To Appear). 2023 [PDF] [Video] [arXiv]
  12. Journal Paper
    Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
    , , , ,
    Applied Energy. 2023 [PDF]

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