Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148487
Title: Reinforcement learning in online principal­-agent problems
Authors: Yue, Ming Long
Keywords: Social sciences::Economic theory::Microeconomics
Science::Mathematics::Statistics
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Yue, M. L. (2021). Reinforcement learning in online principal­-agent problems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148487
Abstract: The 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.
URI: https://hdl.handle.net/10356/148487
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)

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