Título:	RLBoost: Boosting Supervised Models using Deep Reinforcement Learning
Autores:	Eloy Anguiano Batanero, Ángela Fernández Pascual, Álvaro Barbero Jiménez
Año:	2023

Data quality or evaluation can sometimes be a task as important as collecting a large volume of data when it comes to generating accurate artificial intelligence models. Being able to evaluate the data can lead to a larger database that is better suited to a particular problem because we have the ability to filter out data of dubious quality obtained automatically. In this paper, we present RLBoost (Anguiano et al., 2024) [1], an algorithm that uses deep reinforcement learning strategies to evaluate a particular dataset and obtain a model capable of estimating the quality of any new data in order to improve the final predictive quality of a supervised learning model. This solution has the advantage of being agnostic regarding the supervised model used and, through multi-attention strategies, takes into account the data in its context and not only individually. The results of the article show that this model obtains better and more stable results than other state-of-the-art algorithms such as Leave One Out, Shapley or Data Valuation using Reinforcement Learning.

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