Use of the knowledge which is independence on reward in reinforcement learning

Authors:
Yoshiki Miyazaki;Kentarou Kurashige
Affiliations:
University of Computer Science & Systems Engineering, Muroran Institute of Technology, Hokkaido, Japan;Department of Computer Science & Systems Engineering, Muroran Institute of Technology, Hokkaido, Japan
Venue:
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Year:
2009

Citing 3
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Abstract

Now, there are some techniques called machine learning, and reinforcement learning is one of the machine learning which often used for actual machine. In this study, we pay attention to the knowledge that does not depend on a reward in reinforcement learning, and we will improve learning efficiency by using it. Furthermore, we aim at letting agent coping with various tasks under environment where agent is put. In this paper, we propose the knowledge that does not depend on a reward, and we show utility by applying it to the problem that a task turns into under same environment.