When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero engine design.

These embodied agents are typically trainedtabula rasain isolated worlds with limited complexity and diversity. Although highly performant, theyare specialist models that do not generalize beyond a narrowsetoftasks.

Each algorithm optimises its parameters with respect to an objective, such as Q-learning or policy gradient, that defines its semantics.