Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling dynamical systems without requiring models of the system dynamics.

Our contributions are twofold: First, we establish a statistically rigorous definition of competence that generalizesthecommon notion ofclassifier confidence; second, wepresent theALICE (Accurate Layerwise Interpretable Competence Estimation) Score, a pointwise competence estimator foranyclassifier.

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Besides, we observe that existing works still cannotdirectlypropagate the gradient information from deep layers to shallow layers.

Determinantal point processes (DPPs) are well-suited to recommender systems where the goal is to generate collections of diverse, high-quality items.