| Toward self‐driving processes: A deep reinforcement learning approach to control S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, R Bhushan Gopaluni AIChE Journal 65 (10), e16689, 2019 | 153* | 2019 |
| Deep reinforcement learning with shallow controllers: An experimental application to PID tuning NP Lawrence, MG Forbes, PD Loewen, DG McClement, JU Backström, ... Control Engineering Practice 121, 105046, 2022 | 64 | 2022 |
| Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey RB Gopaluni, A Tulsyan, B Chachuat, B Huang, JM Lee, F Amjad, ... IFAC-PapersOnLine 53, 218-229, 2020 | 29 | 2020 |
| Almost Surely Stable Deep Dynamics NP Lawrence, PD Loewen, MG Forbes, JU Backstrom, RB Gopaluni Advances in Neural Information Processing Systems 33, 18942--18953, 2020 | 22 | 2020 |
| Optimal PID and antiwindup control design as a reinforcement learning problem NP Lawrence, GE Stewart, PD Loewen, MG Forbes, JU Backstrom, ... IFAC-PapersOnLine 53, 236-241, 2020 | 21 | 2020 |
| Meta-reinforcement learning for the tuning of PI controllers: An offline approach DG McClement, NP Lawrence, JU Backström, PD Loewen, MG Forbes, ... Journal of Process Control 118, 139-152, 2022 | 18* | 2022 |
| Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system TM Alabi, NP Lawrence, L Lu, Z Yang, RB Gopaluni Applied Energy 333, 120633, 2023 | 14 | 2023 |
| Reinforcement Learning based Design of Linear Fixed Structure Controllers NP Lawrence, GE Stewart, PD Loewen, MG Forbes, JU Backstrom, ... IFAC-PapersOnLine 53, 230-235, 2020 | 9 | 2020 |
| A meta-reinforcement learning approach to process control DG McClement, NP Lawrence, PD Loewen, MG Forbes, JU Backström, ... IFAC-PapersOnLine 54 (3), 685-692, 2021 | 7 | 2021 |
| Meta-reinforcement learning for adaptive control of second order systems DG McClement, NP Lawrence, MG Forbes, PD Loewen, JU Backström, ... 2022 IEEE International Symposium on Advanced Control of Industrial …, 2022 | 3 | 2022 |
| A modular framework for stabilizing deep reinforcement learning control NP Lawrence, PD Loewen, S Wang, MG Forbes, RB Gopaluni IFAC-PapersOnLine 56 (2), 8006-8011, 2023 | 2 | 2023 |
| Machine learning for industrial sensing and control: A survey and practical perspective NP Lawrence, SK Damarla, JW Kim, A Tulsyan, F Amjad, K Wang, ... Control Engineering Practice 145, 105841, 2024 | 1 | 2024 |
| Application of simple random search approach for reinforcement learning to controller tuning parameters N Lawrence, PD Loewen, B Gopaluni, GE Stewart US Patent 11,307,562, 2022 | 1 | 2022 |
| Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior NP Lawrence, PD Loewen, S Wang, MG Forbes, RB Gopaluni Automatica 164, 111642, 2024 | | 2024 |
| Reinforcement Learning with Partial Parametric Model Knowledge S Wang, PD Loewen, NP Lawrence, MG Forbes, RB Gopaluni IFAC-PapersOnLine 56 (2), 8012-8017, 2023 | | 2023 |
| Deep reinforcement learning agents for industrial control system design NP Lawrence University of British Columbia, 2023 | | 2023 |
| Method and system for directly tuning PID parameters using a simplified actor-critic approach to reinforcement learning N Lawrence, PD Loewen, B Gopaluni, GE Stewart US Patent 11,500,337, 2022 | | 2022 |
| Process controller with meta-reinforcement learning DG McClement, NP Lawrence, PD Loewen, RB Gopaluni, MG Forbes, ... US Patent App. 17/653,175, 2022 | | 2022 |
| Approximately Optimal Fixed-Structure Controllers Using Neural Networks D McClement, NP Lawrence, P Loewen, M Forbes, J Backstrom, ... | | 2021 |
| Data-Driven Process Control via Reinforcement Learning and Recurrent Neural Networks NP Lawrence, PD Loewen, GE Stewart, RB Gopaluni Foundations of Process Analytics and Machine Learning, 2019 | | 2019 |
Bhushan GopaluniUniversity of British columbiaVerified email at ubc.ca
