Udenze, Adrian and McDonald-Maier, Klaus (2009) Direct Reinforcement Learning for Autonomous Power Configuration and Control in Wireless Networks. In: 2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2009-07-29 - 2009-08-01.
Udenze, Adrian and McDonald-Maier, Klaus (2009) Direct Reinforcement Learning for Autonomous Power Configuration and Control in Wireless Networks. In: 2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2009-07-29 - 2009-08-01.
Udenze, Adrian and McDonald-Maier, Klaus (2009) Direct Reinforcement Learning for Autonomous Power Configuration and Control in Wireless Networks. In: 2009 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2009-07-29 - 2009-08-01.
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 Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 18 Sep 2013 19:33 |
| Last Modified: | 30 Oct 2024 19:16 |
| URI: | http://repository.essex.ac.uk/id/eprint/6859 |