Inverse optimal control for modeling virus mutations in SARS-CoV-2
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- dc.contributor.author Meijer, Ilse
- dc.date.accessioned 2023-01-31T19:06:46Z
- dc.date.available 2023-01-31T19:06:46Z
- dc.date.issued 2022
- dc.description Treball fi de màster de: Master in Intelligent Interactive Systemsca
- dc.description Tutors: Vicenç Gómez, Mario Ceresa, Antonio Puertas Gallardo
- dc.description.abstract Inverse 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.ca
- dc.format.mimetype application/pdf*
- dc.identifier.uri http://hdl.handle.net/10230/55505
- dc.language.iso engca
- dc.rights ement-NoComercial-SenseObraDerivada 4.0 Internacional → dc.rights This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licenseca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0ca
- dc.subject.keyword Inverse Reinforcement Learning
- dc.subject.keyword Inverse Optimal Control
- dc.subject.keyword OptV
- dc.subject.keyword LMDP
- dc.subject.keyword SARS-CoV-2
- dc.subject.keyword Mutation
- dc.title Inverse optimal control for modeling virus mutations in SARS-CoV-2ca
- dc.type info:eu-repo/semantics/masterThesisca