Research Repository

Direct Reinforcement Learning for autonomous power configuration and control in wireless networks

UNSPECIFIED (2009) Direct Reinforcement Learning for autonomous power configuration and control in wireless networks. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.

Abstract

In this paper, non deterministic Direct Reinforcement Learning (RL) for controlling the transmission times and power of a Wireless Sensor Network (WSN) node is presented. RL allows for truly autonomous optimal behaviour of agents by requiring no models or supervision to learn. Optimal actions are learnt by repeated interactions with the environment. Performance results are presented for Monte Carlo, TD0 and TD?. The resultant optimal learned policies are shown to out perform static power control in a stochastic environment. © 2009 Crown.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - 2009 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2009
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 18 Sep 2013 19:33
Last Modified: 07 Jan 2019 11:16
URI: http://repository.essex.ac.uk/id/eprint/6859

Actions (login required)

View Item View Item