Publication:
Learning from Noisy and Delayed Rewards The Value of Reinforcement Learning to Defense Modeling and Simulation

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Authors
Alt, Jonathan K.
Subjects
reinforcement learning
architecture
agents
autonomous systems
Advisors
Darken, Christian J.
Date of Issue
2012-09
Date
Sep-12
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Modeling and simulation of military operations requires human behavior models capable of learning from experi-ence in complex environments where feedback on action quality is noisy and delayed. This research examines the potential of reinforcement learning, a class of AI learning algorithms, to address this need. A novel reinforcement learning algorithm that uses the exponentially weighted average reward as an action-value estimator is described. Empirical results indicate that this relatively straight-forward approach improves learning speed in both benchmark environments and in challenging applied settings. Applications of reinforcement learning in the verification of the re-ward structure of a training simulation, the improvement in the performance of a discrete event simulation scheduling tool, and in enabling adaptive decision-making in combat simulation are presented. To place reinforcement learning within the context of broader models of human information processing, a practical cognitive architecture is devel-oped and applied to the representation of a population within a conflict area. These varied applications and domains demonstrate that the potential for the use of reinforcement learning within modeling and simulation is great.
Type
Thesis
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Department
Modeling, Virtual Environments and Simulation (MOVES)
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Distribution Statement
Approved for public release; distribution is unlimited.
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