Publications

Most 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. Error whitening: Why Gauss-Newton outperforms Newton
      Maricela Best McKay, Nathan P. Lawrence, Brian Wetton, R. Bhushan Gopaluni
      arXiv (Submitted to NeurIPS). 2026.
      [arXiv]
   2. Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control
      Nathan P. Lawrence, Ali Mesbah
      arXiv (Accepted to IFAC World Congress). 2025.
      [arXiv]
2. Journal Papers
   1. A View on Learning Robust Goal-Conditioned Value Functions: Interplay between RL and MPC
      Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni, Ali Mesbah
      Annual Reviews in Control. 2025.
      [Link] [arXiv] | Comment: 36 pages; preprint
   2. Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
      Nathan Lawrence, Philip Loewen, Shuyuan Wang, Michael Forbes, Bhushan Gopaluni
      Automatica. 2024.
      [arXiv]
   3. Machine learning for industrial sensing and control: A survey and practical perspective
      Nathan Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoit Chachuat, Jong Min Lee, Biao Huang, Bhushan Gopaluni
      Control Engineering Practice. 2024.
      [Link] [arXiv]
   4. Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
      Tobi Michael Alabi, Nathan Lawrence, Lin Lu, Zaiyue Yang, Bhushan Gopaluni
      Applied Energy. 2023.
      [arXiv]
   5. Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
      Nathan Lawrence, Michael Forbes, Philip Loewen, Daniel McClement, Johan Backström, Bhushan Gopaluni
      Control Engineering Practice. 2022.
      [Link] [arXiv]
   6. Meta-reinforcement learning for the tuning of PI controllers: An offline approach
      Daniel McClement, Nathan Lawrence, Johan Backström, Philip Loewen, Michael Forbes, Bhushan Gopaluni
      Journal of Process Control. 2022.
      [arXiv]
   7. Toward self-driving processes: A deep reinforcement learning approach to control
      Steven Spielberg, Aditya Tulsyan, Nathan Lawrence, Philip Loewen, Bhushan Gopaluni
      AIChE Journal. 2019.
      [Link] [arXiv]
3. Conference Proceedings
   1. MPCritic: A Plug-and-Play MPC Architecture for Reinforcement Learning
      Nathan P. Lawrence, Thomas Banker, Ali Mesbah
      Conference on Decision and Control. 2025.
      [Link] [arXiv]
   2. Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning
      Thomas Banker, Nathan P. Lawrence, Ali Mesbah
      American Control Conference. 2025.
      [Link] [arXiv] | Comment: Preprint for ACC 2025 tutorial
   3. Guiding Reinforcement Learning with Incomplete System Dynamics
      Shuyuan Wang, Jingliang Duan, Nathan P Lawrence, Philip D Loewen, Michael G Forbes, R Bhushan Gopaluni, Lixian Zhang
      IROS. 2024.
      [Link]
   4. Deep Hankel matrices with random elements
      Nathan Lawrence, Philip Loewen, Shuyuan Wang, Michael Forbes, Bhushan Gopaluni
      Learning for Dynamics \& Control Conference. 2024.
      [arXiv]
   5. Reinforcement learning with partial parametric model knowledge
      Shuyuan Wang, Philip Loewen, Nathan Lawrence, Michael Forbes, Bhushan Gopaluni
      IFAC World Congress. 2023.
      [arXiv]
   6. A modular framework for stabilizing deep reinforcement learning control
      Nathan Lawrence, Philip Loewen, Shuyuan Wang, Michael Forbes, Bhushan Gopaluni
      IFAC World Congress. 2023.
      [arXiv]
   7. Meta-reinforcement learning for adaptive control of second order systems
      Daniel McClement, Nathan Lawrence, Michael Forbes, Philip Loewen, Johan Backström, Bhushan Gopaluni
      IEEE International Symposium on Advanced Control of Industrial Processes. 2022.
      [arXiv]
   8. A meta-reinforcement learning approach to process control
      Daniel McClement, Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backström, Bhushan Gopaluni
      IFAC Symposium on Advanced Control of Chemical Processes. 2021.
      [Link] [arXiv] | Keynote
   9. Reinforcement learning based design of linear fixed structure controllers
      Nathan Lawrence, Gregory Stewart, Philip Loewen, Michael Forbes, Johan Backström, Bhushan Gopaluni
      IFAC World Congress. 2020.
      [Link] [arXiv]
   10. Modern machine learning tools for monitoring and control of industrial processes: A survey
       Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan Lawrence
       IFAC World Congress. 2020.
       [Link] [arXiv]
   11. Optimal PID and antiwindup control design as a reinforcement learning problem
       Nathan Lawrence, Gregory Stewart, Philip Loewen, Michael Forbes, Johan Backström, Bhushan Gopaluni
       IFAC World Congress. 2020.
       [Link] [arXiv]
   12. Almost surely stable deep dynamics
       Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backström, Bhushan Gopaluni
       NeurIPS. 2020.
       [arXiv] | Spotlight
4. Theses
   1. Deep reinforcement learning agents for industrial control system design
      Nathan Lawrence
      The University of British Columbia. 2023.
      [Link]
   2. Convex and nonconvex optimization techniques for the constrained Fermat-Torricelli problem
      Nathan Lawrence
      Portland State University. 2016.
      [Link]