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Reinforcement Learning for Computer Generated Forces using Open-Source Software
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4144-4893
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9595-2471
2019 (English)In: Proceedings of the 2019 Interservice/Industry Training, Simulation, and Education Conference (IITSEC), 2019, p. 1-11, article id 19197Conference paper, Published paper (Refereed)
Abstract [en]

The creation of behavior models for computer generated forces (CGF) is a challenging and time-consuming task, which often requires expertise in programming of complex artificial intelligence algorithms. This makes it difficult for a subject matter expert with knowledge about the application domain and the training goals to build relevant scenarios and keep the training system in pace with training needs. In recent years, machine learning has shown promise as a method for building advanced decision-making models for synthetic agents. Such agents have been able to beat human champions in complex games such as poker, Go and StarCraft. There is reason to believe that similar achievements are possible in the domain of military simulation. However, in order to efficiently apply these techniques, it is important to have access to the right tools, as well as knowledge about the capabilities and limitations of algorithms.   

This paper discusses efficient applications of deep reinforcement learning, a machine learning technique that allows synthetic agents to learn how to achieve their goals by interacting with their environment. We begin by giving an overview of available open-source frameworks for deep reinforcement learning, as well as libraries with reference implementations of state-of-the art algorithms. We then present an example of how these resources were used to build a reinforcement learning environment for a CGF software intended to support training of fighter pilots. Finally, based on our exploratory experiments in the presented environment, we discuss opportunities and challenges related to the application of reinforcement learning techniques in the domain of air combat training systems, with the aim to efficiently construct high quality behavior models for computer generated forces.

Place, publisher, year, edition, pages
2019. p. 1-11, article id 19197
Keywords [en]
Pilot Training, Computer Generated Forces, Machine Learning, Reinforcement Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-162589OAI: oai:DiVA.org:liu-162589DiVA, id: diva2:1376598
Conference
Interservice/Industry Training, Simulation, and Education Conference, December 2-6, 2019, Orlando, USA
Funder
Vinnova, 2017-04885Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-12-09 Created: 2019-12-09 Last updated: 2021-04-20

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Källström, JohanHeintz, Fredrik

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Output format
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