Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to tackle a much wider range of data. Standard imitation learning fails when learning from demonstrations with varying optimality, and only learns suboptimal policies. Previous works use confidence scores or rankings to capture beneficial information from demonstrations with varying optimality, but they suffer from many limitations, e.g., manually…