Integration of Reinforcement Learning and Unreal Engine for Enemy Containment via Autonomous Swarms

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dc.contributor.authorPeterson, David
dc.contributor.authorAndrades, Beyonce
dc.contributor.authorLizarazu-Ampuero, Kevin
dc.contributor.authorDeshmukh, Jai
dc.contributor.authorStapor, Thomas
dc.contributor.authorDestaffan, Will
dc.contributor.authorEngel, Don
dc.contributor.authorKrometis, Justin
dc.contributor.authorKauffman, Justin A.
dc.date.accessioned2023-06-16T15:00:39Z
dc.date.available2023-06-16T15:00:39Z
dc.date.issued2023-01-19
dc.descriptionAIAA SCITECH 2023 Forum, National Harbor, MD, 23-27 January 2023.en_US
dc.description.abstractMaritime remote sensing (MRS) is a multi-disciplinary and multi-physics field at the intersection of naval hydrodynamics, physical oceanography, overhead platforms, and electro-optical sensors. One proposed improvement to MRS information gathering and operations is the use of swarms of autonomous surface, aerial, and/or undersea vehicles as a multi-agent system (MAS) to automate data collection, data processing, and situational awareness. Here, we explore the design of an autonomous multi-agent system with the objective of containing a target object, i.e., surrounding the object in a loosely defined shape. The agents make decisions using reinforcement learning by way of a Markov decision process. Our current proof-of-concepts are modeled using Python-based 2D simulation environments which contain our agents and target used for prototyping and testing various reward functions.. However, we have built an infrastructure to port the simulation environments to Unreal Engine 4 for increased fidelity. In the current modeled scenario, each agent's decisions are based on global positional knowledge of each entity in the environment. Future iterations are planned to feature agent decision making based on a high-fidelity communication protocol and inputs from integrated sensors.en_US
dc.description.sponsorshipThis work is possible via a gift from the MITRE Corporation.en_US
dc.description.urihttps://arc.aiaa.org/doi/10.2514/6.2023-2674en_US
dc.format.extent15 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2kuyw-ayrv
dc.identifier.citationDavid Peterson, Beyonce Andrades, Kevin Lizarazu-Ampuero, Jai Deshmukh, Thomas Stapor, Will Destaffan, Don Engel, Justin Krometis and Justin A. Kauffman. "Integration of Reinforcement Learning and Unreal Engine for Enemy Containment via Autonomous Swarms," AIAA 2023-2674. AIAA SCITECH 2023 Forum. January 2023. https://doi.org/10.2514/6.2023-2674en_US
dc.identifier.urihttps://doi.org/10.2514/6.2023-2674
dc.identifier.urihttp://hdl.handle.net/11603/28223
dc.language.isoen_USen_US
dc.publisherAIAAen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Imaging Research Center (IRC)
dc.relation.ispartofUMBC Center for Space Sciences and Technology (CSST) / Center for Research and Exploration in Space Sciences & Technology II (CRSST II)
dc.relation.ispartofUMBC Office for the Vice President of Research & Creative Achievement (ORCA)
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleIntegration of Reinforcement Learning and Unreal Engine for Enemy Containment via Autonomous Swarmsen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-2838-0140en_US

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UMBC Computer Science and Electrical Engineering Department
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