In this blog post, we aim to understand if, when and why offline RL is a better approach for tackling a variety of sequential decision-making problems.
A computational tool developed to predict the structure of protein complexes is providing new insights into the biomolecular mechanisms of their function.
To better understand advances in AI as a part of the education of health sciences students, researchers conducted a comprehensive literature review and hosted a virtual panel.
Unsupervised reinforcement learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization.
imodels provides a simple unified interface and implementation for many state-of-the-art interpretable modeling techniques, particularly rule-based methods.
Research collaborators propose that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge.