Before revision:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized training data for training our reward function.
After revision:  In this paper, we overcome the limitations of prior work by developing a novel computational technique that infers an idealized reward function from suboptimal demonstration and bootstraps suboptimal demonstrations to synthesize optimality-parameterized data to train an idealized reward function.