ensemble-disagreement

This paper evaluates Disagreement-Regularized Imitation Learning (DRIL) for image-based continuous control in CarRacing, comparing Gaussian and Beta learner policies. Score-selected DRIL shows the largest gains in few-demonstration settings, while Beta behavior cloning remains competitive with more demonstrations.