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Learning algorithms for Markov decision processes

Published online by Cambridge University Press:  14 July 2016

Masami Kurano
Masami Kurano*
Affiliation:
Chiba University
*
∗Postal address: Department of Mathematics, Faculty of Education, Chiba University, Yayoi-cho, Chiba 260, Japan.
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Abstract

This study is concerned with finite Markov decision processes whose dynamics and reward structure are unknown but the state is observable exactly.

We establish a learning algorithm which yields an optimal policy and construct an adaptive policy which is optimal under the average expected reward criterion.

Keywords

ADAPTIVE CONTROLAVERAGE REWARD CRITERION

Information

Type
Short Communications
Information
Journal of Applied Probability , Volume 24 , Issue 1 , March 1987 , pp. 270 - 276
Copyright
Copyright © Applied Probability Trust 1987 

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References

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