  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. This enables our model to learn from the optimal behavior and
filter out the suboptimal behavior of each demonstrator. Our model learns a
single policy that can outperform even the best demonstrator, and can be used
to estimate the expertise of any demonstrator at any state. We illustrate our
findings on real-robotic continuous control tasks from Robomimic and discrete
environments such as MiniGrid and chess, out-performing competing methods in
$21$ out of $23$ settings, with an average of $7\%$ and up to $60\%$
improvement in terms of the final reward.
