Almost Surely Stable Deep Dynamics. - Nathan P. Lawrence; Philip D. Loewen; Michael G. Forbes; Johan U. Backström; R. Bhushan Gopaluni (NeurIPS, 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)

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)

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)

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)

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)

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)

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)

Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey. - R. Bhushan Gopaluni; Aditya Tulsyan; Benoît Chachuat; Biao Huang 001; Jong Min Lee 002; Faraz Amjad; Seshu Kumar Damarla; Jong Woo Kim; Nathan P. Lawrence (CoRR, 2022)

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)

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)

Reinforcement Learning with Partial Parametric Model Knowledge. - Shuyuan Wang; Philip D. Loewen; Nathan P. Lawrence; Michael G. Forbes; R. Bhushan Gopaluni (CoRR, 2023)

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)

Deep Hankel matrices with random elements. - Nathan P. Lawrence; Philip D. Loewen; Shuyuan Wang; Michael G. Forbes; R. Bhushan Gopaluni (L4DC, 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)

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 024; Benoît Chachuat; Jong Min Lee 002; Biao Huang 001; R. Bhushan Gopaluni (CoRR, 2024)

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 002 (CoRR, 2025)

Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning. - Thomas Banker; Nathan P. Lawrence; Ali Mesbah 002 (CoRR, 2025)

MPCritic: A plug-and-play MPC architecture for reinforcement learning. - Nathan P. Lawrence; Thomas Banker; Ali Mesbah 002 (CoRR, 2025)

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks. - Pritam Dash; Ethan Chan; Nathan P. Lawrence; Karthik Pattabiraman (CoRR, 2025)