Generative Adversarial Imitation Learning: Advantages & Limits

A growing number of AI projects rely on learning a mapping between observations and actions. For strategic and technical reasons, learning from demonstrations will play a crucial role in developing several use cases (robots, video games, self-driving vehicles). In my latest project, I had the chance to gain a solid understanding of Generative Adversarial Imitation Learning (GAIL). As part of a team, my goal was to use GAIL to help a robot predict and understand human behaviors for safety purposes. In this article, I will explain Generative Adversarial Imitation Learning, introduce its advantages and explain the limits of this approach.