Nathan Lawrence

Nathan Lawrence

Nathan is a PhD student in applied mathematics at UBC. 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.

๐Ÿ“š Program/Degree
PhD | Started 2018
๐Ÿ“ Research
Deep Reinforcement Learning Algorithms for Maintenance-free Control in Industrial Applications
๐Ÿ‘ฅ Also supervised by
๐Ÿ“จ Contact
๐Ÿ’ป GitHub
๐Ÿ”— Website

DAIS Lab Publications

  1. Journal Paper Top 10% Most Downloaded
    Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control
    , , , ,
    AIChE Journal. 2019 [PDF]
  2. Conference Proceedings NeurIPS Spotlight
    Almost Surely Stable Deep Dynamics
    , , ,
    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
    , , , , ,
    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Video] [arXiv]
  4. Conference Proceedings
    Reinforcement Learning based Design of Linear Fixed Structure Controllers
    , , , , ,
    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
    , , , , , , , ,
    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
    , , , ,
    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
    , , , , ,
    Control Engineering Practice. 2021 [PDF]

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