USING NEURAL NETWORKS TO DETERMINE COURSE OF ACTION FOR A LAND-BASED CONSTRUCTIVE SIMULATION
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Authors
Lian, Weiwen Mervyn
Subjects
neural network
course of action
course of action
Advisors
Darken, Christian J.
Date of Issue
2019-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The latest breakthroughs achieved by various commercial companies in applying neural networks to games have led to research in applying such technologies to the military domain. This thesis uses a proxy stochastic simulation environment to identify the factors required to train a neural network via reinforcement learning to maneuver forces in a battlefield and overcome hostile forces. An incremental training approach was used to train the neural network to recognize the enemy first, before training it to consolidate forces as a form of emergent behavior. Eight neural networks were trained successfully to maneuver forces to engage the enemies. Seven of these neural networks managed to generalize their training to search for enemies in a larger area than the scenario in which they were trained. The neural networks also showed some success in performing force consolidation before attacking an enemy. The most important neural network hyperparameters that contributed to training success were training duration, learning rate, discount factor, and loss factor. This thesis also found that the neural network reward function and randomizing starting positions during the training phase are critical to training success.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
Sponsors
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Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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Copyright is reserved by the copyright owner.