EDAIL: Adversarial Imitation Learning Via Exploration-Driven Data Augmentation
Adversarial Imitation Learning (AIL) is a prominent paradigm in imitation learning that enables policy acquisition from expert demonstrations without relying on manually crafted reward functions. Although AIL has achieved promising results in certain scenarios, many existing methods suffer from mode…