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Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II


May 11, 2022
Nicholas Waytowich, James Hare, Vinicius G. Goecks, Mark Mittrick, John Richardson, Anjon Basak, Derrik E. Asher

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* Submitted to the 2022 SPIE Defense + Commercial Sensing (DCS) Conference on "Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV" 

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Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination


Mar 17, 2022
Derrik E. Asher, Anjon Basak, Rolando Fernandez, Piyush K. Sharma, Erin G. Zaroukian, Christopher D. Hsu, Michael R. Dorothy, Thomas Mahre, Gerardo Galindo, Luke Frerichs, John Rogers, John Fossaceca

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* 23 pages, 3 figures, 60 references, Review Paper 

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Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training


Jul 29, 2021
Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, Derrik E. Asher

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* Published at: Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462K (2021), SPIE Defense + Commercial Sensing, 2021, Online Only 
* This article appeared in the news at: https://www.army.mil/article/247261/army_researchers_develop_innovative_framework_for_training_ai 

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