Reviews: Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets

The paper describes a new learning model able to discover'intentions' from expert policies by using an imitation learning framework. The idea is mainly based on the GAIL model which aims at learning by imitation a policy using a GAN approach. The main difference in the article is that the learned policy is, in fact, a mixture of sub-policies, each sub-policy aiming at automatically matching a particular intention in the expert behavior. The GAIL algorithm is thus derived with this mixture, resulting in an effective learning technique. Another approach is also proposed where the intention will be captured through a latent vector by derivating the InfoGAN algorithm for this particular case.