InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
Published in
Neural Information Processing Systems(2017)External Links:
TL
TL;DR
A new algorithm is proposed that can infer the latent structure of expert demonstrations in an unsupervised way, built on top of Generative Adversarial Imitation Learning, and can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations.