  It has been a challenge to learning skills for an agent from long-horizon
unannotated demonstrations. Existing approaches like Hierarchical Imitation
Learning(HIL) are prone to compounding errors or suboptimal solutions. In this
paper, we propose Option-GAIL, a novel method to learn skills at long horizon.
The key idea of Option-GAIL is modeling the task hierarchy by options and train
the policy via generative adversarial optimization. In particular, we propose
an Expectation-Maximization(EM)-style algorithm: an E-step that samples the
options of expert conditioned on the current learned policy, and an M-step that
updates the low- and high-level policies of agent simultaneously to minimize
the newly proposed option-occupancy measurement between the expert and the
agent. We theoretically prove the convergence of the proposed algorithm.
Experiments show that Option-GAIL outperforms other counterparts consistently
across a variety of tasks.
