  Imitation Learning is a sequential task where the learner tries to mimic an
expert's action in order to achieve the best performance. Several algorithms
have been proposed recently for this task. In this project, we aim at proposing
a wide review of these algorithms, presenting their main features and comparing
them on their performance and their regret bounds.
