Extended spatial and temporal learning scale in reinforcement learning

Authors:
Hui Zhu;N. Mastorakis;X. D. Zhuang
Affiliations:
College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China and WSEAS Research Group in Qingdao, Qingdao University, China;Department of Computer Science, Military Institutions of University Education, Hellenic Naval Academy, Piraeus, Greece;WSEAS Research Group in Qingdao, Qingdao University, China
Venue:
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
Year:
2010

Citing 3
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Abstract

In this paper, the extended learning scale is proposed to improve the efficiency of reinforcement learning. The learning scale is defined and its impact on the performance of learning is investigated. Based on the correlation of the spatial or temporal neighboring states, fuzzy state and artificial ant colony are incorporated into reinforcement learning for the extension of learning scale. In the simulation experiments, the proposed learning methods with extended learning scale are applied in a robot path planning problem. The experimental results indicate that the extension of spatial and temporal learning scale improves the learning efficiency.