I’m a PhD candidate in Applied Mathematics at the University of British Columbia (UBC) under the supervision of Dr. Philip Loewen and Dr. Bhushan Gopaluni. I’m interested in developing actionable control methods based on deep reinforcement learning for real-world applications.

News

We are organizing a half-day workshop at AdCONIP 2022 on reinforcement learning to be held on August 7th 2022. Further details on the conference webpage.

Publications

Most of my papers are linked to arXiv or a DOI. If you can’t find one, feel free to get in touch. You can also find my articles on my Google Scholar profile.

1. Preprints
   1. Meta Reinforcement Learning for Adaptive Control: An Offline Approach
      Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni
      arXiv.org 2022.
      [Link]
2. Journal Papers
   1. Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
      Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Daniel G. McClement, Johan U. Backström, R. Bhushan Gopaluni
      Control Engineering Practice. 2022.
      [Link] [arXiv]
   2. Toward self‐driving processes: A deep reinforcement learning approach to control
      Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni
      AIChE Journal. 2019.
      [Link] [arXiv]
3. Conference Proceedings
   1. A Meta-Reinforcement Learning Approach to Process Control
      Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni
      IFAC-PapersOnLine. 2021.
      [Link] [arXiv] | Keynote Paper
   2. Reinforcement Learning based Design of Linear Fixed Structure Controllers
      Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
      IFAC World Congress. 2020.
      [Link] [arXiv]
   3. Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
      Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
      IFAC World Congress. 2020.
      [Link] [arXiv]
   4. Almost Surely Stable Deep Dynamics
      Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni
      NeurIPS. 2020.
      [Link] [arXiv] | NeurIPS Spotlight
   5. Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
      R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
      IFAC World Congress. 2020.
      [Link]
4. Theses
   1. Convex and Nonconvex Optimization Techniques for the Constrained Fermat-Torricelli Problem
      Nathan Lawrence
      Portland State University. 2016.
      [Link]