  Adversarial methods for imitation learning have been shown to perform well on
various control tasks. However, they require a large number of environment
interactions for convergence. In this paper, we propose an end-to-end
differentiable adversarial imitation learning algorithm in a Dyna-like
framework for switching between model-based planning and model-free learning
from expert data. Our results on both discrete and continuous environments show
that our approach of using model-based planning along with model-free learning
converges to an optimal policy with fewer number of environment interactions in
comparison to the state-of-the-art learning methods.
