Adversarial Imitation Learning with Trajectorial Augmentation and Correction
This addresses the challenge of limited expert data in control tasks, but it is incremental as it builds on existing adversarial imitation learning methods.
The paper tackles the problem of requiring many expert demonstrations for deep imitation learning by introducing a novel data augmentation method that preserves trajectory success, using a correction network and adversarial training. Experiments show improved accuracy and convergence time while maintaining trajectory diversity.
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 the augmented trajectories. To achieve this, we introduce a semi-supervised correction network that aims to correct distorted expert actions. To adequately test the abilities of the correction network, we develop an adversarial data augmented imitation architecture to train an imitation agent using synthetic experts. Additionally, we introduce a metric to measure diversity in trajectory datasets. Experiments show that our data augmentation strategy can improve accuracy and convergence time of adversarial imitation while preserving the diversity between the generated and real trajectories.