Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment

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dc.contributor.authorYuksek, Burak
dc.contributor.authorDemirezen, Umut M.
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2021-01-28T11:43:53Z
dc.date.available2021-01-28T11:43:53Z
dc.date.issued2021-01-04
dc.description.abstractIn this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agenten_UK
dc.identifier.citationYuksek B, Umut Demirezen M, Inalhan G. (2021) Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment. In: AIAA SciTech Forum 2021, 11-15 and 19-21 January 2021, Onlineen_UK
dc.identifier.urihttps://doi.org/10.2514/6.2021-0175
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16274
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.relation.ispartofseries;AIAA 2021-0175
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectModeling and Simulation Technologiesen_UK
dc.titleDevelopment of UCAV fleet autonomy by reinforcement learning in a wargame simulation environmenten_UK
dc.typeConference paperen_UK

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