  Automated Reinforcement Learning (AutoRL) is a relatively new area of
research that is gaining increasing attention. The objective of AutoRL consists
in easing the employment of Reinforcement Learning (RL) techniques for the
broader public by alleviating some of its main challenges, including data
collection, algorithm selection, and hyper-parameter tuning. In this work, we
propose a general and flexible framework, namely ARLO: Automated Reinforcement
Learning Optimizer, to construct automated pipelines for AutoRL. Based on this,
we propose a pipeline for offline and one for online RL, discussing the
components, interaction, and highlighting the difference between the two
settings. Furthermore, we provide a Python implementation of such pipelines,
released as an open-source library. Our implementation has been tested on an
illustrative LQG domain and on classic MuJoCo environments, showing the ability
to reach competitive performances requiring limited human intervention. We also
showcase the full pipeline on a realistic dam environment, automatically
performing the feature selection and the model generation tasks.
