Indirect Reinforcement Learning for Autonomous Power Configuration and Control in Wireless Networks

被引：0

作者：

Udenze, Adrian ^{[1
]}

McDonald-Maier, Klaus ^{[1
]}

机构：

[1] Univ Essex, Sch Comp Sci & Elect Engn, Embedded & Intelligent Syst Res Grp, Colchester CO4 3SQ, Essex, England

来源：

PROCEEDINGS OF THE 2009 NASA/ESA CONFERENCE ON ADAPTIVE HARDWARE AND SYSTEMS | 2009年

关键词：

D O I：

10.1109/AHS.2009.51

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, non deterministic Indirect Reinforcement Learning (RL) techniques for controlling the transmission times and power of Wireless Network nodes are presented. Indirect RL facilitates planning and learning which ultimately leads to convergence on optimal actions with reduced episodes or time steps compared to direct RL. Three Dyna architecture based algorithms for non deterministic environments are presented. The results show improvements over direct RL and conventional static power control techniques.

引用

页码：297 / 304

页数：8

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