ARLO: A Framework for Automated Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 37 of its 62 references that are in the map. First posted on arXiv 20 May 2022.
Builds on
37 of 62 references.
References · 37
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25 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.