Ms. Mikayla Timm Profile

Mikayla Timm

at Systems & Technology Research

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Proceedings Article | 12 June 2023 Presentation + Paper

Curriculum-heavy reinforcement learning for multi-domain operations

Nicholas Pioch, Lucas Sheldon, Thomas Harris, Matt Henry, Andrew Spisak, Mikayla Timm

Proceedings Volume 12538, 1253819 (2023) https://doi.org/10.1117/12.2663299

KEYWORDS: Machine learning, Feature extraction, Ecosystems, Deep learning, Artificial intelligence, Software development

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We present a framework for developing software agents via Machine Learning (ML) entitled Curriculum-Heavy Accelerated Learning in a Competitive Environment (CHALICE). CHALICE is designed to train and deploy intelligent agents capable of executing strategies for air-ground combat as embodied in AFRL’s MIST turn-based wargame system. Such agents can be used to suggest courses of action in real-time to operational planners and to provide an adversarial opponent for evaluation of proposed courses of action. CHALICE uses state-of-the-art Deep Neural Networks (DNNs) to represent the state of the environment and Deep Reinforcement Learning (DRL) to train each agent via repeated feedback from outcomes of the MIST Stratagem game. Unlike recent DRL approaches for strategy games such as Go or StarCraft [1] [2], CHALICE minimizes dependence on existing corpora of human gameplay and trains efficiently with low computational resources and short convergence time (hours to days rather than weeks to months). Over the course of four government-led competitions, CHALICE produced agents that continually improved their performance, resulting in competitive play against human and automated opposing agents at relatively low training cost and time. In this paper, we motivate the operational problem and technical challenges, provide an overview of our technical approach, elaborate on our vision-based and graph-based DNN architecture design and agent training procedure, and present results from the most recent Stratagem competition. We close with a discussion of future research recommendations.

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