Domain Adaptation for Imitation Learning Using Generative Adversarial Network

Domain Adaptation for Imitation Learning Using Generative Adversarial Network

Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expe...

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Main Authors: Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan, Eiji Kamioka
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Sensors
Subjects:
imitation learning
domain adaptive imitation learning
generative adversarial network
Online Access:https://www.mdpi.com/1424-8220/21/14/4718
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spelling doaj-948bdeb9c5c5496ba84e76dd68c598732021-07-23T14:05:27ZengMDPI AGSensors1424-82202021-07-01214718471810.3390/s21144718Domain Adaptation for Imitation Learning Using Generative Adversarial NetworkTho Nguyen Duc0Chanh Minh Tran1Phan Xuan Tan2Eiji Kamioka3Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, JapanGraduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, JapanDepartment of Information and Communications Engineering, Shibaura Institute of Technology, Tokyo 135-8548, JapanGraduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, JapanImitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional.https://www.mdpi.com/1424-8220/21/14/4718imitation learningdomain adaptive imitation learninggenerative adversarial network
collection DOAJ
language English
format Article
sources DOAJ
author Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
spellingShingle Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
Domain Adaptation for Imitation Learning Using Generative Adversarial Network
Sensors
imitation learning
domain adaptive imitation learning
generative adversarial network
author_facet Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
author_sort Tho Nguyen Duc
title Domain Adaptation for Imitation Learning Using Generative Adversarial Network
title_short Domain Adaptation for Imitation Learning Using Generative Adversarial Network
title_full Domain Adaptation for Imitation Learning Using Generative Adversarial Network
title_fullStr Domain Adaptation for Imitation Learning Using Generative Adversarial Network
title_full_unstemmed Domain Adaptation for Imitation Learning Using Generative Adversarial Network
title_sort domain adaptation for imitation learning using generative adversarial network
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-07-01
description Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional.
topic imitation learning
domain adaptive imitation learning
generative adversarial network
url https://www.mdpi.com/1424-8220/21/14/4718
work_keys_str_mv AT thonguyenduc domainadaptationforimitationlearningusinggenerativeadversarialnetwork
AT chanhminhtran domainadaptationforimitationlearningusinggenerativeadversarialnetwork
AT phanxuantan domainadaptationforimitationlearningusinggenerativeadversarialnetwork
AT eijikamioka domainadaptationforimitationlearningusinggenerativeadversarialnetwork
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