  Automated Machine Learning (AutoML) has become increasingly popular in recent
years due to its ability to reduce the amount of time and expertise required to
design and develop machine learning systems. This is very important for the
practice of machine learning, as it allows building strong baselines quickly,
improving the efficiency of the data scientists, and reducing the time to
production. However, despite the advantages of AutoML, it faces several
challenges, such as defining the solutions space and exploring it efficiently.
Recently, some approaches have been shown to be able to do it using tree-based
search algorithms and context-free grammars. In particular, GramML presents a
model-free reinforcement learning approach that leverages pipeline
configuration grammars and operates using Monte Carlo tree search. However, one
of the limitations of GramML is that it uses default hyperparameters, limiting
the search problem to finding optimal pipeline structures for the available
data preprocessors and models. In this work, we propose an extension to GramML
that supports larger search spaces including hyperparameter search. We
evaluated the approach using an OpenML benchmark and found significant
improvements compared to other state-of-the-art techniques.
