  Human beings are able to understand objectives and learn by simply observing
others perform a task. Imitation learning methods aim to replicate such
capabilities, however, they generally depend on access to a full set of optimal
states and actions taken with the agent's actuators and from the agent's point
of view. In this paper, we introduce a new algorithm - called Disentangling
Generative Adversarial Imitation Learning (DisentanGAIL) - with the purpose of
bypassing such constraints. Our algorithm enables autonomous agents to learn
directly from high dimensional observations of an expert performing a task, by
making use of adversarial learning with a latent representation inside the
discriminator network. Such latent representation is regularized through mutual
information constraints to incentivize learning only features that encode
information about the completion levels of the task being demonstrated. This
allows to obtain a shared feature space to successfully perform imitation while
disregarding the differences between the expert's and the agent's domains.
Empirically, our algorithm is able to efficiently imitate in a diverse range of
control problems including balancing, manipulation and locomotive tasks, while
being robust to various domain differences in terms of both environment
appearance and agent embodiment.
