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.
“Explainable AI is not something you produce or consume. It’s an educational experience, and the bottom line is that we need to focus on helping the humans to solve problems.”
Damaging earthquakes can strike at any time. While we can’t prevent them from occurring, we can make sure casualties, economic loss and disruption of essential services are kept to a minimum.
With our proposed dataset and multi-task, multi-domain learning approach, we have shown one potential avenue for making diverse datasets reusable in robotics.