  GAIL is a recent successful imitation learning architecture that exploits the
adversarial training procedure introduced in GANs. Albeit successful at
generating behaviours similar to those demonstrated to the agent, GAIL suffers
from a high sample complexity in the number of interactions it has to carry out
in the environment in order to achieve satisfactory performance. We
dramatically shrink the amount of interactions with the environment necessary
to learn well-behaved imitation policies, by up to several orders of magnitude.
Our framework, operating in the model-free regime, exhibits a significant
increase in sample-efficiency over previous methods by simultaneously a)
learning a self-tuned adversarially-trained surrogate reward and b) leveraging
an off-policy actor-critic architecture. We show that our approach is simple to
implement and that the learned agents remain remarkably stable, as shown in our
experiments that span a variety of continuous control tasks. Video
visualisations available at: \url{https://youtu.be/-nCsqUJnRKU}.
