Domain adaptation-based transfer learning using adversarial networks

• Published in: Knowledge engineering review Vol. 35
• Main Authors: Shoeleh, Farzaneh, Yadollahi, Mohammad Mehdi, Asadpour, Masoud
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 2020
• Subjects: Adaptation; Algorithms; Domains; Knowledge; Machine learning; Methods; Skills
• ISSN: 0269-8889; 1469-8005
• DOI: 10.1017/S0269888920000107

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.