Time-aware Hierarchical EM Energy-based Subsequence Learning for Inducing Offline Decision-making Policies with Heterogeneous Reward Functions.

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dc.contributor.advisorMin Chi, Chair
dc.contributor.advisorDavid Roberts, Member
dc.contributor.advisorRanga Vatsavai, Member
dc.contributor.advisorXipeng Shen, Member
dc.contributor.authorYang, Xi
dc.date.accepted2022-11-09
dc.date.accessioned2022-11-11T13:30:29Z
dc.date.available2022-11-11T13:30:29Z
dc.date.defense2022-05-02
dc.date.issued2022-05-02
dc.date.released2022-11-11
dc.date.reviewed2022-08-23
dc.date.submitted2022-08-19
dc.degree.disciplineComputer Science
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Computer Science.
dc.formatPh.D. North Carolina State University, 2022.
dc.identifier.otherdeg30828
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/40085
dc.titleTime-aware Hierarchical EM Energy-based Subsequence Learning for Inducing Offline Decision-making Policies with Heterogeneous Reward Functions.
dcterms.extent1 online resource (viii, 82 pages) : illustrations

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