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Indirect Reinforcement Learning for autonomous power configuration and control in Wireless Networks

Udenze, A and McDonald-Maier, K (2009) Indirect Reinforcement Learning for autonomous power configuration and control in Wireless Networks. In: UNSPECIFIED, ? - ?.

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Abstract

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. © 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 19:35
Last Modified: 04 Feb 2019 11:15
URI: http://repository.essex.ac.uk/id/eprint/6860

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