  Reinforcement Learning (RL) controllers have generated excitement within the
control community. The primary advantage of RL controllers relative to existing
methods is their ability to optimize uncertain systems independently of
explicit assumption of process uncertainty. Recent focus on engineering
applications has been directed towards the development of safe RL controllers.
Previous works have proposed approaches to account for constraint satisfaction
through constraint tightening from the domain of stochastic model predictive
control. Here, we extend these approaches to account for plant-model mismatch.
Specifically, we propose a data-driven approach that utilizes Gaussian
processes for the offline simulation model and use the associated posterior
uncertainty prediction to account for joint chance constraints and plant-model
mismatch. The method is benchmarked against nonlinear model predictive control
via case studies. The results demonstrate the ability of the methodology to
account for process uncertainty, enabling satisfaction of joint chance
constraints even in the presence of plant-model mismatch.
