March 13, 2024, 4:42 a.m. | Shiliang Zuo

cs.LG updates on

arXiv:2403.07143v1 Announce Type: cross
Abstract: This work studies the repeated principal-agent problem from an online learning perspective. The principal's goal is to learn the optimal contract that maximizes her utility through repeated interactions, without prior knowledge of the agent's type (i.e., the agent's cost and production functions).
I study three different settings when the principal contracts with a $\textit{single}$ agent each round: 1. The agents are heterogeneous; 2. the agents are homogenous; 3. the principal interacts with the same agent …

abstract agent agents arxiv cost cs.lg design functions her interactions knowledge learn online learning perspective perspectives prior production studies team through type utility work

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