Nathan P. Lawrence

According to our database, Nathan P. Lawrence authored at least 24 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Error whitening: Why Gauss-Newton outperforms Newton.

Maricela Best McKay, Nathan P. Lawrence, Brian Wetton, R. Bhushan Gopaluni

CoRR, May, 2026

Soft MPCritic: Amortized Model Predictive Value Iteration.

Thomas Banker, Nathan P. Lawrence, Ali Mesbah

CoRR, April, 2026

Stability-constrained policy optimization under unknown rewards.

Thomas Banker, Nathan P. Lawrence, Ali Mesbah

IFAC J. Syst. Control., 2026

2025

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control.

Nathan P. Lawrence, Ali Mesbah

CoRR, December, 2025

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks.

Pritam Dash, Ethan Chan, Nathan P. Lawrence, Karthik Pattabiraman

CoRR, June, 2025

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

Annu. Rev. Control., 2025

MPCritic: A Plug-and-Play MPC Architecture for Reinforcement Learning.

Nathan P. Lawrence, Thomas Banker, Ali Mesbah

Proceedings of the 64th IEEE Conference on Decision and Control, 2025

Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning.

Thomas Banker, Nathan P. Lawrence, Ali Mesbah

Proceedings of the 2025 American Control Conference, 2025

2024

Machine learning for industrial sensing and control: A survey and practical perspective.

Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim, Aditya Tulsyan, Faraz Amjad, Kai Wang, Benoît Chachuat, Jong Min Lee, Biao Huang, R. Bhushan Gopaluni

CoRR, 2024

Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior.

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni

Autom., 2024

Deep Hankel matrices with random elements.

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 2024

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

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024

2023

Reinforcement Learning with Partial Parametric Model Knowledge.

Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence, Michael G. Forbes, R. Bhushan Gopaluni

CoRR, 2023

A modular framework for stabilizing deep reinforcement learning control.

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes, R. Bhushan Gopaluni

CoRR, 2023

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 P. Lawrence, Lin Lu, Zaiyue Yang, R. Bhushan Gopaluni

CoRR, 2023

2022

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey.

R. Bhushan Gopaluni, Aditya Tulsyan, Benoît Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence

CoRR, 2022

Meta-Reinforcement Learning for Adaptive Control of Second Order Systems.

Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen, Johan U. Backström, R. Bhushan Gopaluni

CoRR, 2022

Meta Reinforcement Learning for Adaptive Control: An Offline Approach.

Daniel G. McClement, Nathan P. Lawrence, Johan U. Backström, Philip D. Loewen, Michael G. Forbes, R. Bhushan Gopaluni

CoRR, 2022

2021

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

CoRR, 2021

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

CoRR, 2021

2020

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. Backström, R. Bhushan Gopaluni

CoRR, 2020

Reinforcement Learning based Design of Linear Fixed Structure Controllers.

Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni

CoRR, 2020

Deep Reinforcement Learning for Process Control: A Primer for Beginners.

Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni

CoRR, 2020

Almost Surely Stable Deep Dynamics.

Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backström, R. Bhushan Gopaluni

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020