Constrained Reinforcement Learning for Process Optimization and Control
Abstract
Dynamic real-time optimization (DRTO) of chemical processes is a challenging task. The main bottlenecks in the industrial application of DRTO are the presence of uncertainty, highly nonlinear behaviour, and long sampling times. Chemical processes present the following challenges: 1) plant-model mismatch, 2) process disturbances, 3) constraints for safe operation. To solve these problems, reinforcement learning (RL) would be a natural alternative, as it effectively addresses stochastic environments. Unfortunately, present RL algorithms fail to reliably satisfy state constraints. We propose a constrained RL algorithm which guarantees the satisfaction of joint chance constraints. To accomplish this, we propose 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, which can, therefore, guarantee the satisfaction of joint chance constraints.
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About CPSE
The Centre for Process Systems Engineering (CPSE) is a multi- institutional research centre. It was inaugurated in August 1989 by Professor Roger W.H. Sargent and involves Imperial College London and University College London. Innovative and dynamic in its approach to research and development, CPSE’s accomplishments include the Queens Prize for Higher Education, presented in 2003 for research excellence and technology transfer. Three spin-out companies have been established. One of these, Process Systems Enterprise Limited (PSE), received the Royal Academy of Engineering’s MacRobert Award in 2007; this is the UK’s highest award for innovation in engineering. The Centre continues to broaden the scope of its activities over all size scales enabling all areas of process systems to be addressed. This enables CPSE to be responsive to the evolving needs of industry and society.