(143b) A practitioner’s assessment of Deep Reinforcement Learning for industrial process control applications

Reinforcement Learning (RL) is a machine learning technology in which a computer agent learns, through trial and error, the best way to accomplish a particular task [1]. The recent development of Deep Reinforcement Learning (DRL) technology, in which deep learning networks [2] are used to parameterize the RL agent’s policy and value functions, has enabled superhuman performance for some tasks, most especially games such as chess and GO [3]. Researchers have devised DRL algorithms specifically for continuous control problems [4,5], and this has led many in the process control community to wonder what impact DRL technology will have on our industry. Two recent papers provide an introduction to DRL technology and an analysis of its appropriateness for industrial process control applications [6,7]. This presentation builds on the previous analysis efforts by proposing a that an industrial process control technology must be:

• Intelligent
• Consistent
• Offset-free
• Nominally stable
• Flexible

DRL technology will be assessed with respect to each requirement, exposing existing deficiencies, useful research directions, and potential solutions. It will be shown that DRL technology is not likely to replace currently used Proportional-Integral-Derivative (PID) or Model-Predictive Control (MPC) algorithms, however it may prove useful in managing control systems by helping to tune controllers [8,9,10], advising operators during upsets or transient operations [6], and by managing plant-wide disturbances such as diurnal changes and weather events [6].

A final section will outline several promising research directions for DRL technology [11].

References

[1] RS Sutton, AG Barto, “Reinforcement Learning – An Introduction”, The MIT Press, (2018).

[2] I Goodfellow, Y Bengio, A Courville, “Deep Learning”, The MIT Press, (2016).

[3] D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, J Schrittwieser, I. Antonoglou, V Panneershelvam, M Lanctot, “Mastering the game of Go with deep neural networks and tree search”, Nature 529, 484–489 (2017).

[4] TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, D Wierstra, “Continuous control with deep reinforcement learning”, arXiv:1509.02971 (2015).

[5] L Busoniu, T deBruin, D Tolic, J Kober, I Palunko, “Reinforcement learning for control: Performance, stability, and deep approximators”, Annual Reviews in Control, 46, 8-28, (2018).

[6] J Shin, TA Badgwell, KH Liu, JH Lee, “Reinforcement Learning – Overview of recent progress and implications for process control”, Computers & Chemical Engineering, 127, 282-294 (2019).

[7] RN Nian, J Liu, B Huang, “A review on reinforcement learning: Introduction and applications in industrial process control”, Computers & Chemical Engineering 139, 106886 (2020).

[8] T Badgwell, K Liu, N Subrahmanya, M Kovalski, “Adaptive PID controller tuning via deep reinforcement learning”, US Patent 10,915,073, (2021).

[9] O Dogru, K Velswamy, F Ibrahim, Y Wu, AS Sundaramoorthy, B Huang, S Xu, M Nixon, N Bell, “Reinforcement learning approach to autonomous PID tuning”, Computers & Chemical Engineering, 161, 107760 (2022).

[10] NP Lawrence, MG Forbes, PD Loewen, DG McClement, JU Backstrom, RB Gopaluni, “Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning”, arXiv, 2111.07171v1 (2021).

[11] A Mesbah, KP Wabersich, AP Schoellig, MN Zeilinger, S Lucia, TA Badgwell, JP Paulson, “Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?”, American Control Conference, June 8-10, Atlanta, GA (2022).