Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

Non-independent Intelligent Creatures Reinforcement Learning Mechanism Research Based on I-XCS

Authors
PengJian Xi, Jianxiong Tan
Corresponding Author
PengJian Xi
Available Online July 2015.
DOI
https://doi.org/10.2991/icismme-15.2015.9How to use a DOI?
Keywords
artificial intelligence; Non-independent intelligent creatures;I-XCS;gradient descent; reinforcement learning
Abstract
In order to solve many problems of reinforcement learning of Non-independent intelligent creatures in artificial intelligence, such as the single MDP environment and narrow learning space. This paper designed an Non-independent intelligent creatures reinforcement learning mechanism based on the Improved XCS classifier. This learning mechanism based on the original XCS classification capabilities and online knowledge, it constructs a high-stability, low-dimensional approximation method by using the gradient descent related technologies. This method has low-storage ability and enhances the inductive learning ability of intelligent creatures. Simulation experiment results show that the I-XCS classification learning algorithm not only can efficiently solve MDP environment issues such as single, narrow space, but also to a certain extent improved the analysis of non-independent intelligent creatures in reinforcement learning performance.
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Proceedings
First International Conference on Information Sciences, Machinery, Materials and Energy
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-67-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/icismme-15.2015.9How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - PengJian Xi
AU  - Jianxiong Tan
PY  - 2015/07
DA  - 2015/07
TI  - Non-independent Intelligent Creatures Reinforcement Learning Mechanism Research Based on I-XCS
BT  - First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 47
EP  - 54
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.9
DO  - https://doi.org/10.2991/icismme-15.2015.9
ID  - Xi2015/07
ER  -