ArticleACS engineering Au2026
PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning.
Article in ACS engineering Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Efficient data selection is critical in domains where data acquisition is expensive and time-consuming, such as material science. In this work, we introduce a novel active learning framework that integrates proximal policy optimization (PPO) with Gaussian process regression (GPR) to strategically select informative data points and thereby enhance predictive modeling. Leveraging the inherent stability and sample efficiency of PPO, achieved through a clipped surrogate objective, the framework guides data acquisition via a custom-designed Gymnasium environment tailored for GPR. In this environment, the PPO agent dynamically chooses data points based on their potential to improve the GPR's performance, as measured by the
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