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Domain adaptation-based transfer learning using adversarial networks

Part of: Adaptive Learning Agents 2019

Published online by Cambridge University Press:  26 February 2020

Farzaneh Shoeleh Open the ORCID record for Farzaneh Shoeleh [Opens in a new window] ,
Mohammad Mehdi Yadollahi  and
Masoud Asadpour
Farzaneh Shoeleh
Affiliation:
University of New Brunswick, Fredericton, New Brunswick, Canada e-mails: fshoeleh@unb.ca, mehdiyadollahi@unb.ca
Mohammad Mehdi Yadollahi
Affiliation:
University of New Brunswick, Fredericton, New Brunswick, Canada e-mails: fshoeleh@unb.ca, mehdiyadollahi@unb.ca
Masoud Asadpour
Affiliation:
University of Tehran, Tehran, Iran e-mail: asadpour@ut.ac.ir
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Abstract

There is an implicit assumption in machine learning techniques that each new task has no relation to the tasks previously learned. Therefore, tasks are often addressed independently. However, in some domains, particularly reinforcement learning (RL), this assumption is often incorrect because tasks in the same or similar domain tend to be related. In other words, even though tasks are quite different in their specifics, they may have general similarities, such as shared skills, making them related. In this paper, a novel domain adaptation-based method using adversarial networks is proposed to do transfer learning in RL problems. Our proposed method incorporates skills previously learned from source task to speed up learning on a new target task by providing generalization not only within a task but also across different, but related tasks. The experimental results indicate the effectiveness of our method in dealing with RL problems.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 35 , 2020 , e7
Copyright
© Cambridge University Press, 2020

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