Adversarial Imitation Learning with Trajectorial Augmentation and Correction
Dafni Antotsiou,Carlo Ciliberto,Tae-Kyun Kim,Dafni Antotsiou,Carlo Ciliberto,Tae-Kyun Kim
Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks. A way to overcome this shortage of labels is through data augmentation. However, this cannot be easily applied to control tasks due to the sequential nature of the problem. In this work, we introduce a novel augmentation method which preserves the success of ...
