  Imitation Learning describes the problem of recovering an expert policy from
demonstrations. While inverse reinforcement learning approaches are known to be
very sample-efficient in terms of expert demonstrations, they usually require
problem-dependent reward functions or a (task-)specific reward-function
regularization. In this paper, we show a natural connection between inverse
reinforcement learning approaches and Optimal Transport, that enables more
general reward functions with desirable properties (e.g., smoothness). Based on
our observation, we propose a novel approach called Wasserstein Adversarial
Imitation Learning. Our approach considers the Kantorovich potentials as a
reward function and further leverages regularized optimal transport to enable
large-scale applications. In several robotic experiments, our approach
outperforms the baselines in terms of average cumulative rewards and shows a
significant improvement in sample-efficiency, by requiring just one expert
demonstration.
