Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management

dc.contributor.author Bihl, Trevor
dc.contributor.author Jones, Aaron
dc.contributor.author Farr, Patrick
dc.contributor.author Straub, Kayla
dc.contributor.author Bontempo, Brian
dc.contributor.author Jones, Frank
dc.date.accessioned 2021-12-24T18:11:43Z
dc.date.available 2021-12-24T18:11:43Z
dc.date.issued 2022-01-04
dc.description.abstract Unmanned aerial vehicles (UAVs) have applications in search and rescue operations and such operations could be more efficient by using appropriate artificial intelligence (AI) to enable a UAV agent to operate autonomously. Sensor resource management (SRM), which leverages capabilities across location intelligence, facilitates the efficient and effective use of UAVs and their sensors to complete a set of tasks. Furthermore, multiple UAVs, each with different sensor configurations, must be considered when maximizing mission effects. Instantiating operational autonomy for such teams requires considerable coordination. One AI approach relevant to this task is multi-agent reinforcement learning (MARL). However, MARL has seen limited prior use in SRM. This work evaluates the trade-space of MARL algorithms with respect to performing heterogeneous sensor resource management (SRM) tasks, considers the concept of evaluating MARL in a test and evaluation framework and compares a suit of algorithms with random and Bayesian hyperparameter optimization methods.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.695
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/80034
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Location Intelligence Research in System Sciences
dc.subject autonomy
dc.subject location analysis
dc.subject reinforcement learning
dc.subject search and rescue
dc.subject sensor resource management
dc.title Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management
dc.type.dcmi text
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