Meijer, Ilse2023-01-312023-01-312022http://hdl.handle.net/10230/55505Treball fi de màster de: Master in Intelligent Interactive SystemsTutors: Vicenç Gómez, Mario Ceresa, Antonio Puertas GallardoInverse Optimal Control (IOC) deals with the problem of recovering an unknown cost function in a Markov decision process from expert demonstrations acting optimally. In this thesis we apply IOC to SARS-CoV-2 data. For our application we use the (mutated) sequences found in SARS-CoV-2 data as the expert demonstrations. We present a way to learn useful state representations for this data, and successfully apply IOC on a special class of Markov decision processes which allow for an efficient computation of the value and cost functions of the states, and informative 2D representations of the state.application/pdfengement-NoComercial-SenseObraDerivada 4.0 Internacional → dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licenseinfo:eu-repo/semantics/masterThesisInverse Reinforcement LearningInverse Optimal ControlOptVLMDPSARS-CoV-2Mutationinfo:eu-repo/semantics/openAccess