  Providing expert trajectories in the context of Imitation Learning is often
expensive and time-consuming. The goal must therefore be to create algorithms
which require as little expert data as possible. In this paper we present an
algorithm that imitates the higher-level strategy of the expert rather than
just imitating the expert on action level, which we hypothesize requires less
expert data and makes training more stable. As a prior, we assume that the
higher-level strategy is to reach an unknown target state area, which we
hypothesize is a valid prior for many domains in Reinforcement Learning. The
target state area is unknown, but since the expert has demonstrated how to
reach it, the agent tries to reach states similar to the expert. Building on
the idea of Temporal Coherence, our algorithm trains a neural network to
predict whether two states are similar, in the sense that they may occur close
in time. During inference, the agent compares its current state with expert
states from a Case Base for similarity. The results show that our approach can
still learn a near-optimal policy in settings with very little expert data,
where algorithms that try to imitate the expert at the action level can no
longer do so.
