  In this work we formulate and treat an extension of the Imitation from
Observations problem. Imitation from Observations is a generalisation of the
well-known Imitation Learning problem where state-only demonstrations are
considered. In our treatment we extend the scope of Imitation from Observations
to feature-only demonstrations which could arguably be described as partial
observations. Therewith we mean that the full state of the decision makers is
unknown and imitation must take place on the basis of a limited set of
features. We set out for methods that extract an executable policy directly
from those features which, in the literature, would be referred to as
Behavioural Cloning methods. Our treatment combines elements from probability
and information theory and draws connections with entropy regularized Markov
Decision Processes.
