Generative Adversarial Network for Imitation Learning from Single Demonstration
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 disadvan...
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| Format: | Article |
| Language: | English |
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University of Baghdad, College of Science for Women
2021-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6652 |
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| author | Tho Nguyen Duc Chanh Minh Tran Phan Xuan Tan Eiji Kamioka |
| author_facet | Tho Nguyen Duc Chanh Minh Tran Phan Xuan Tan Eiji Kamioka |
| author_sort | Tho Nguyen Duc |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b35fe766d19e47bca6ddc933aa07bd72 |
| institution | Matheson Library |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-b35fe766d19e47bca6ddc933aa07bd722025-08-02T17:09:58ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-12-01184(Suppl.)10.21123/bsj.2021.18.4(Suppl.).1350Generative Adversarial Network for Imitation Learning from Single DemonstrationTho Nguyen Duc0Chanh Minh Tran1Phan Xuan Tan2Eiji Kamioka3School of Engineering and Science, Shibaura Institute of Technology, Japan.School of Engineering and Science, Shibaura Institute of Technology, JapanSchool of Engineering and Science, Shibaura Institute of Technology, Japan.School of Engineering and Science, Shibaura Institute of Technology, JapanImitation 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.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6652Deep Learning, Few-shot Learning, Generative Adversarial Network, Imitation Learning, One-shot Learning |
| spellingShingle | Tho Nguyen Duc Chanh Minh Tran Phan Xuan Tan Eiji Kamioka Generative Adversarial Network for Imitation Learning from Single Demonstration مجلة بغداد للعلوم Deep Learning, Few-shot Learning, Generative Adversarial Network, Imitation Learning, One-shot Learning |
| title | Generative Adversarial Network for Imitation Learning from Single Demonstration |
| title_full | Generative Adversarial Network for Imitation Learning from Single Demonstration |
| title_fullStr | Generative Adversarial Network for Imitation Learning from Single Demonstration |
| title_full_unstemmed | Generative Adversarial Network for Imitation Learning from Single Demonstration |
| title_short | Generative Adversarial Network for Imitation Learning from Single Demonstration |
| title_sort | generative adversarial network for imitation learning from single demonstration |
| topic | Deep Learning, Few-shot Learning, Generative Adversarial Network, Imitation Learning, One-shot Learning |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6652 |
| work_keys_str_mv | AT thonguyenduc generativeadversarialnetworkforimitationlearningfromsingledemonstration AT chanhminhtran generativeadversarialnetworkforimitationlearningfromsingledemonstration AT phanxuantan generativeadversarialnetworkforimitationlearningfromsingledemonstration AT eijikamioka generativeadversarialnetworkforimitationlearningfromsingledemonstration |