Generative Adversarial Network for Imitation Learning from Single Demonstration

Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan, Eiji Kamioka

研究成果: Article査読


Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, a Generative Adversarial Network-based model is proposed which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing considered tasks despite the limitation in the number of expert demonstrations, which clearly indicate the potential of our model.

ジャーナルBaghdad Science Journal
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 化学 (全般)
  • 数学 (全般)
  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(その他)
  • 物理学および天文学(全般)


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