Nathan P. Lawrence

According to our database, Nathan P. Lawrence authored at least 14 papers between 2020 and 2024.

Collaborative distances:

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

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

2023

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

CoRR, 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