  We study how an autonomous agent learns to perform a task from demonstrations
in a different domain, such as a different environment or different agent. Such
cross-domain imitation learning is required to, for example, train an
artificial agent from demonstrations of a human expert. We propose a scalable
framework that enables cross-domain imitation learning without access to
additional demonstrations or further domain knowledge. We jointly train the
learner agent's policy and learn a mapping between the learner and expert
domains with adversarial training. We effect this by using a mutual information
criterion to find an embedding of the expert's state space that contains
task-relevant information and is invariant to domain specifics. This step
significantly simplifies estimating the mapping between the learner and expert
domains and hence facilitates end-to-end learning. We demonstrate successful
transfer of policies between considerably different domains, without extra
supervision such as additional demonstrations, and in situations where other
methods fail.
