Global overview of Imitation Learning
arXiv:1801.06503v179 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental survey paper that synthesizes existing knowledge for researchers in the field.
The paper provides a comprehensive review of Imitation Learning algorithms, comparing their features, performance, and regret bounds.
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.