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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5342

Title: Applying reinforcement learning to the weapon assignment problem in air defence
Authors: Mouton, H
Roodt, J
Le Roux, H
Keywords: Weapon assignment
Air defence
Reinforcement learning
Q-learning algorithm
Military
Issue Date: Dec-2011
Publisher: Scientia Militaria: Stellenbosch University
Citation: Mouton, H, Roodt, J, Le Roux, H. 2011. Applying reinforcement learning to the weapon assignment problem in air defence. Scientia Militaria, South African Journal of Military Studies, Vol 39(2), pp 1-15
Series/Report no.: Workflow request;7607
Abstract: The modern battlefield is a fast-paced, information-rich environment, where discovery of intent, situation awareness and the rapid evolution of concepts of operation and doctrine are critical success factors. A combination of the techniques investigated and tested in this work, together with other techniques in Artificial Intelligence (AI) and modern computational techniques, may hold the key to relieving the burden of the decision-maker and aiding in better decision-making under pressure. The techniques investigated in this article were two methods from the machine-learning subfield of reinforcement learning (RL), namely a Monte Carlo (MC) control algorithm with exploring starts (MCES), and an off-policy temporal-difference (TD) learning-control algorithm, Q-learning. These techniques were applied to a simplified version of the weapon assignment (WA) problem in air defence. The MCES control algorithm yielded promising results when searching for an optimal shooting order. A greedy approach was taken in the Q-learning algorithm, but experimentation showed that the MCES-control algorithm still performed significantly better than the Q-learning algorithm, even though it was slower.
Description: Scientia Militaria, South African Journal of Military Studies, Vol 39(2), pp 123-140
URI: http://scientiamilitaria.journals.ac.za/pub/article/view/115
http://hdl.handle.net/10204/5342
ISSN: 2224-0020
Appears in Collections:Safety and security
Command control and information warfare
General science, engineering & technology

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