Imitation Learning by Estimating Expertise of Demonstrators

Understand

Many existing imitation learning datasets are collected from multiple demonstrators, each with different expertise at different parts of the environment.

• Yet, standard imitation learning algorithms typically treat all demonstrators as homogeneous, regardless of their expertise, absorbing the weaknesses of any suboptimal demonstrators.
• In this work, we show that unsupervised learning over demonstrator expertise can lead to a consistent boost in the performance of imitation learning algorithms.
• We develop and optimize a joint model over a learned policy and expertise levels of the demonstrators.