  Reward function specification, which requires considerable human effort and
iteration, remains a major impediment for learning behaviors through deep
reinforcement learning. In contrast, providing visual demonstrations of desired
behaviors often presents an easier and more natural way to teach agents. We
consider a setting where an agent is provided a fixed dataset of visual
demonstrations illustrating how to perform a task, and must learn to solve the
task using the provided demonstrations and unsupervised environment
interactions. This setting presents a number of challenges including
representation learning for visual observations, sample complexity due to high
dimensional spaces, and learning instability due to the lack of a fixed reward
or learning signal. Towards addressing these challenges, we develop a
variational model-based adversarial imitation learning (V-MAIL) algorithm. The
model-based approach provides a strong signal for representation learning,
enables sample efficiency, and improves the stability of adversarial training
by enabling on-policy learning. Through experiments involving several
vision-based locomotion and manipulation tasks, we find that V-MAIL learns
successful visuomotor policies in a sample-efficient manner, has better
stability compared to prior work, and also achieves higher asymptotic
performance. We further find that by transferring the learned models, V-MAIL
can learn new tasks from visual demonstrations without any additional
environment interactions. All results including videos can be found online at
\url{https://sites.google.com/view/variational-mail}.
