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Hierarchical Reinforcement Learning Model for Military Simulations

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3 Author(s)
A. S. Sidhu ; Intelligent System Centre (IntelliSys), Level 7, Border X Block, Research Techno Plaza, Nanyang Technological University (NTU), Singapore, 637553. e-mail: amandeep@ntu.edu.sg ; N. S. Chaudhari ; Ghee Ming Goh

Majority of the actions in army are hierarchical and occur simultaneously with some other action. Mission of an echelon is sub-divided into sub-missions which are assigned to the lower echelon. These lower echelons pursue their missions simultaneously. To apply reinforcement learning to such highly concurrent actions' domain as military, we propose a concurrent options model for a set of temporally extended actions that may not terminate at the same time and trigger the next transition without any regard for the other sub-options. We provide formal representation of the model.

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The 2006 IEEE International Joint Conference on Neural Network Proceedings

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