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dc.contributor.authorEllingsen, Nikolai Kjærem
dc.identifier.citationEllingsen, N. K. (2020) Imitation Accelerated Q-learning on a Simulated Formula Student Driverless Racecar (Master's thesis). University of Agder, Grimstaden_US
dc.descriptionMaster's thesis in Information- and communication technology (IKT590)en_US
dc.description.abstractIn the international Formula Student competition, only a handful compete in the driverless category. Most of them using expensive hardware such as LIDAR’s. By leveraging reinforcement learning, a cheaper camera based system can be created .In order to train this system a simulator based on a fork of Microsoft’s AirSim by Formula Technion was used. A virtual replica of a Formula Student car designed for 2020 by Align Racing UiA, functioned as the test vehicle. In order to decrease the required training time, a pre-trained imitation learning network was used. This was implemented into a Deep Q-Learning network in four different methods. The most successful method was able to accelerate the learning process by 36%.en_US
dc.publisherUniversity of Agderen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleImitation Accelerated Q-learning on a Simulated Formula Student Driverless Racecaren_US
dc.typeMaster thesisen_US
dc.rights.holder© 2020 Nikolai Kjærem Ellingsenen_US
dc.subject.nsiVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550en_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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