  Consider an imitation learning problem that the imitator and the expert have
different dynamics models. Most of the current imitation learning methods fail
because they focus on imitating actions. We propose a novel state
alignment-based imitation learning method to train the imitator to follow the
state sequences in expert demonstrations as much as possible. The state
alignment comes from both local and global perspectives and we combine them
into a reinforcement learning framework by a regularized policy update
objective. We show the superiority of our method on standard imitation learning
settings and imitation learning settings where the expert and imitator have
different dynamics models.
