SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models
Shenghua Wan Yucen Wang Minghao Shao Ruying Chen De-Chuan Zhan
Model-based imitation learning (MBIL) is a popular reinforcement learning
method that improves sample efficiency on high-dimension input sources, such as
images and videos. Following the convention of MBIL research, existing
algorithms are highly deceptive by task-irrelevant information, especially
moving distractors in videos. To tackle this problem, we propose a new
algorithm - named Separated Model-based Adversarial Imitation Learning (SeMAIL)
- decoupling the environment dynamics into two parts by task-relevant
dependency, which is determined by agent actions, and training separately. In
this way, the agent can imagine its trajectories and imitate the expert
behavior efficiently in task-relevant state space. Our method achieves
near-expert performance on various visual control tasks with complex
observations and the more challenging tasks with different backgrounds from
expert observations.