Chance constrained policy optimization for process control and optimization. (March 2022)

• Record Type: Journal Article
• Authors: Petsagkourakis, Panagiotis; Sandoval, Ilya Orson; Bradford, Eric; Galvanin, Federico; Zhang, Dongda; Rio-Chanona, Ehecatl Antonio del
• Is Part Of: Journal of process control. Volume 111(2022)
• Page Start: 35
• Page End: 45
• Publication Date: 2022-03
• Subjects: Policy search -- Reinforcement Learning -- Data-driven process control -- Chance constraints -- Bayesian Optimization; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.01.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Chemical process optimization and control are affected by (1) plant-model mismatch, (2) process disturbances, and (3) constraints for safe operation. Reinforcement learning by policy optimization would be a natural way to solve this due to its ability to address stochasticity, plant-model mismatch, and directly account for the effect of future uncertainty and its feedback in a proper closed-loop manner; all without the need of an inner optimization loop. One of the main reasons why reinforcement learning has not been considered for industrial processes (or almost any engineering application) is that it lacks a framework to deal with safety critical constraints. Present algorithms for policy optimization use difficult-to-tune penalty parameters, fail to reliably satisfy state constraints or present guarantees only in expectation. We propose a chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability — which is crucial for safety critical tasks. This is achieved by the introduction of constraint tightening (backoffs), which are computed simultaneously with the feedback policy. Backoffs are adjusted with Bayesian optimization using the empirical cumulative distribution function of the probabilistic constraints, and are therefore self-tuned. This results in a general methodology that can be imbued into present policy optimization algorithms to enable them to satisfy joint chance constraints with high probability. We present case studies that analyse the performance of the proposed approach. Highlights: Chance constrained policy optimization technique has been constructed. Bayesian optimization and reinforcement learning have been combined to produce a safe policy for use. Case studies with parametric and structural uncertainty have been considered. Offline optimal policy that can be used online.