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رؤى - Machine Learning - # Incentivized Learning in Bandit Games

Principal-Agent Bandit Games: Incentivized Learning Unveiled


المفاهيم الأساسية
Principal-Agent Bandit Games introduce Incentivized Learning to optimize utility.
الملخص
  • The article introduces a framework for repeated principal-agent bandit games.
  • Misaligned objectives between principal and agent are addressed through incentives.
  • The principal aims to maximize utility by learning optimal incentive policies.
  • Algorithms for regret minimization in multi-armed and contextual settings are presented.
  • Theoretical guarantees are supported by numerical experiments.
  • The work bridges mechanism design and learning aspects in principal-agent models.
  • Contextual bandit setting broadens applicability in various domains.
  • Lower bounds for regret in bandit settings are discussed.
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الإحصائيات
"Nearly optimal (with respect to a horizon T) learning algorithms for the principal’s regret in both multi-armed and linear contextual settings." "The overall algorithm achieves both nearly optimal distribution-free and instance-dependent regret bounds." "Contextual IPA achieves a O(d √ T log(T)) regret bound."
اقتباسات
"The principal aims to iteratively learn an incentive policy to maximize her own total utility." "Our work focuses on the blend of mechanism design and learning." "The overall algorithm achieves nearly optimal regret bounds."

الرؤى الأساسية المستخلصة من

by Antoine Sche... في arxiv.org 03-07-2024

https://arxiv.org/pdf/2403.03811.pdf
Incentivized Learning in Principal-Agent Bandit Games

استفسارات أعمق

질문 1

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질문 2

제안된 알고리즘의 효과에 에이전트 측의 불확실성이 미치는 영향은 무엇인가요? Answer 2 here

질문 3

주요-에이전트 밴딧 게임의 정보 임대 개념을 어떻게 다룰 수 있을까요? Answer 3 here
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