MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

Understand

Imitation learning has been widely applied to various autonomous systems thanks to recent development in interactive algorithms that address covariate shift and compounding errors induced by traditional approaches like behavior cloning.

• However, existing interactive imitation learning methods assume access to one perfect expert.
• Whereas in reality, it is more likely to have multiple imperfect experts instead.
• In this paper, we propose MEGA-DAgger, a new DAgger variant that is suitable for interactive learning with multiple imperfect experts.