Open AccessGenerative Adversarial Network for Imitation Learning from Single Demonstration
Author(s) -
Tho Nguyen Duc,
Chanh Minh Tran,
Phan Xuan Tan,
Eiji Kamioka
Publication year - 2021
Publication title -
mağallaẗ baġdād li-l-ʿulūm
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.167
H-Index - 6
eISSN - 2411-7986
pISSN - 2078-8665
DOI - 10.21123/bsj.2021.18.4(suppl.).1350
Subject(s) - imitation , generative grammar , adversarial system , computer science , artificial intelligence , task (project management) , disadvantage , machine learning , order (exchange) , generative adversarial network , deep learning , engineering , psychology , social psychology , systems engineering , finance , economics
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, we propose a Generative Adversarial Network-based model 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.