  Generative adversarial learning is a popular new approach to training
generative models which has been proven successful for other related problems
as well. The general idea is to maintain an oracle $D$ that discriminates
between the expert's data distribution and that of the generative model $G$.
The generative model is trained to capture the expert's distribution by
maximizing the probability of $D$ misclassifying the data it generates.
Overall, the system is \emph{differentiable} end-to-end and is trained using
basic backpropagation. This type of learning was successfully applied to the
problem of policy imitation in a model-free setup. However, a model-free
approach does not allow the system to be differentiable, which requires the use
of high-variance gradient estimations. In this paper we introduce the Model
based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach
for the problem of adversarial imitation learning. We show how to use a forward
model to make the system fully differentiable, which enables us to train
policies using the (stochastic) gradient of $D$. Moreover, our approach
requires relatively few environment interactions, and fewer hyper-parameters to
tune. We test our method on the MuJoCo physics simulator and report initial
results that surpass the current state-of-the-art.
