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Deep reinforcement learning for autonomous off-road driving in simulation

dc.contributor.advisorZimmermann Karel
dc.contributor.authorValentin Jacques
dc.date.accessioned2018-06-19T22:06:54Z
dc.date.available2018-06-19T22:06:54Z
dc.date.issued2018-06-18
dc.identifierKOS-784543666005
dc.identifier.urihttp://hdl.handle.net/10467/77185
dc.description.abstractThis thesis presents different ways to make a car autonomous. We will use the power of machine learning and neural network to ?teach? a car how to drive autonomously in an off-road environment by using only a minimum set of sensors, in our case which is just a single RGB camera. We will first focus on a technique called imitation learning, it is a supervised learning algorithm which takes a lot of example pairs (image; driving command) to extract a policy that the car will use to drive in unseen situations. Then we will use the so-called reinforcement learning technique. It is an unsupervised learning algorithm which manages, by a lot of trial and error experiments, to create a policy used by the car to drive safely. We managed with these two techniques to make our car drive itself in a simulator.cze
dc.description.abstractThis thesis presents different ways to make a car autonomous. We will use the power of machine learning and neural network to ?teach? a car how to drive autonomously in an off-road environment by using only a minimum set of sensors, in our case which is just a single RGB camera. We will first focus on a technique called imitation learning, it is a supervised learning algorithm which takes a lot of example pairs (image; driving command) to extract a policy that the car will use to drive in unseen situations. Then we will use the so-called reinforcement learning technique. It is an unsupervised learning algorithm which manages, by a lot of trial and error experiments, to create a policy used by the car to drive safely. We managed with these two techniques to make our car drive itself in a simulator.eng
dc.language.isoENG
dc.publisherČeské vysoké učení technické v Praze. Vypočetní a informační centrum.cze
dc.publisherCzech Technical University in Prague. Computing and Information Centre.eng
dc.rightsA university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.html.eng
dc.rightsVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html.cze
dc.subjectSelf Driving Car,Car Simulator,Deep Learning,Imitation Learning,Reinforcement learningcze
dc.subjectSelf Driving Car,Car Simulator,Deep Learning,Imitation Learning,Reinforcement learningeng
dc.titleHluboké učení pro autonomní off-road řízení v simulacicze
dc.titleDeep reinforcement learning for autonomous off-road driving in simulationeng
dc.typeMAGISTERSKÁ PRÁCEcze
dc.typeMASTER'S THESISeng
dc.date.accepted
dc.contributor.refereeEcorchard Gaël Pierre Marie
theses.degree.disciplineKybernetika a robotikacze
theses.degree.grantorkatedra řídicí technikycze
theses.degree.programmeKybernetika a robotikacze


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