State Alignment-based Imitation Learning

Understand

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.