Adversarial Imitation Learning with Trajectorial Augmentation and Correction

• URL: http://arxiv.org/abs/2103.13887v2
• Date: Fri, 26 Mar 2021 11:39:16 GMT
• Title: Adversarial Imitation Learning with Trajectorial Augmentation and Correction
• Authors: Dafni Antotsiou, Carlo Ciliberto and Tae-Kyun Kim
• Abstract summary: We introduce a novel augmentation method which preserves the success of the augmented trajectories. We develop an adversarial data augmented imitation architecture to train an imitation agent using synthetic experts. Experiments show that our data augmentation strategy can improve accuracy and convergence time of adversarial imitation.
• Score: 61.924411952657756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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