  Meta-learning is a branch of machine learning which aims to quickly adapt
models, such as neural networks, to perform new tasks by learning an underlying
structure across related tasks. In essence, models are being trained to learn
new tasks effectively rather than master a single task. Meta-learning is
appealing for process control applications because the perturbations to a
process required to train an AI controller can be costly and unsafe.
Additionally, the dynamics and control objectives are similar across many
different processes, so it is feasible to create a generalizable controller
through meta-learning capable of quickly adapting to different systems. In this
work, we construct a deep reinforcement learning (DRL) based controller and
meta-train the controller using a latent context variable through a separate
embedding neural network. We test our meta-algorithm on its ability to adapt to
new process dynamics as well as different control objectives on the same
process. In both cases, our meta-learning algorithm adapts very quickly to new
tasks, outperforming a regular DRL controller trained from scratch.
Meta-learning appears to be a promising approach for constructing more
intelligent and sample-efficient controllers.
