Using fuzzy reinforcement learning for power control in wireless transmitters

Author

Vengerov, David ; Berenji, Hamid R.

Author_Institution

Intelligent Inference Systems Corp., NASA Ames Research Center, Mountain View, CA, USA

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

797

Lastpage

802

Abstract

Fuzzy set theory was recently shown to be an effective tool for generalizing the learned experience between similar states in reinforcement learning problems with large or continuous state spaces. In our previous work (2001) we presented the first convergence proof for an algorithm combining fuzzy sets and reinforcement learning. In this paper we apply our algorithm to a very challenging wireless power control problem characterized by heavily delayed rewards combined with several sources of randomness. The results show a considerable improvement in performance as compared to the optimal constant power transmission

Keywords

dynamic programming; fuzzy control; learning (artificial intelligence); power control; radio transmitters; actor-critic algorithm; convergence; dynamic programming; fuzzy learning; fuzzy set theory; power control; reinforcement learning; wireless transmitters; Approximation algorithms; Convergence; Function approximation; Fuzzy control; Fuzzy logic; Fuzzy set theory; Learning; Power control; State-space methods; Transmitters;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1005095

Filename

1005095