Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148487
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dc.contributor.authorYue, Ming Longen_US
dc.date.accessioned2021-04-28T01:59:17Z-
dc.date.available2021-04-28T01:59:17Z-
dc.date.issued2021-
dc.identifier.citationYue, M. L. (2021). Reinforcement learning in online principal­-agent problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148487en_US
dc.identifier.urihttps://hdl.handle.net/10356/148487-
dc.description.abstractThe principal-agent problem arises when an entity (the agent) acts or makes decisions on behalf of another (the principal) that goes against the best interests of the principal, typically the result of asymmetric information. To address the problem, the principal can align incentives through the use of appropriate contracts. In this paper, we focus on online principal-agent problems. We propose the utilisation of reinforcement learning methods to allow the principal to learn to generate optimal contracts in an end-to-end fashion, solving the principal-agent problem in a model-free manner without the need for prior knowledge of the environment or explicit modelling of the problem.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectSocial sciences::Economic theory::Microeconomicsen_US
dc.subjectScience::Mathematics::Statisticsen_US
dc.titleReinforcement learning in online principal­-agent problemsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorPUN Chi Sengen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciences and Economicsen_US
dc.contributor.supervisor2Nixie Sapphira Lesmanaen_US
dc.contributor.supervisoremailcspun@ntu.edu.sgen_US
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Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
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