Title: State Alignment-based Imitation Learning. Abstract: Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of existing imitation learning methods fail because they focus on the imitation of actions. We propose a novel state alignment-based imitation learning method to train the imitator by following the state sequences in the expert demonstrations as much as possible. The alignment of states comes from both local and global perspectives. 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 as well as the challenging settings in which the expert and the imitator have different dynamics models.