T/P refers to "tokens" per patient, C/T to codes per "token," and # Codes to the number of unique codes present in the dataset. Missing values reflect quantities not reported in the source publication.

A.1 Algorithm The code is available at https://github.com/mklissa/MOC. A.2 Tabular experiments A.2.1 Implementation Details For our experiments of the FourRooms domain we based our implementation on [Bacon et al., 2016] and ran the experiments for 500 episodes that last a maximum of 1000 steps with goal located in the right hallway. In the first experiment we verify whether learning a fixed set of options can be accelerated by our method. We define this fixed set as the hallway options from Sutton et al. [1999b]. As the policies of these options were deterministic and we use importance sampling, we relax them to stochastic policies where the most likely action is the one leading to a hallway.

In this paper, we present a hyperparameter-free offline safe IL algorithm, SafeDICE, that learns safe policy by leveraging the non-preferred demonstrations in the space of stationary distributions. Our algorithm directly estimates the stationary distribution corrections of the policy that imitate the demonstrations excluding the non-preferred behavior.

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JS is also a special case of a general familyofdivergences,thef-divergences[20].