  Imitation learning algorithms have been interpreted as variants of divergence
minimization problems. The ability to compare occupancy measures between
experts and learners is crucial in their effectiveness in learning from
demonstrations. In this paper, we present tractable solutions by formulating
imitation learning as minimization of the Sinkhorn distance between occupancy
measures. The formulation combines the valuable properties of optimal transport
metrics in comparing non-overlapping distributions with a cosine distance cost
defined in an adversarially learned feature space. This leads to a highly
discriminative critic network and optimal transport plan that subsequently
guide imitation learning. We evaluate the proposed approach using both the
reward metric and the Sinkhorn distance metric on a number of MuJoCo
experiments. For the implementation and reproducing results please refer to the
following repository https://github.com/gpapagiannis/sinkhorn-imitation.
