arXiv cs.AI· Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani·· 11 小时前AI 评分18
当智能体也在学习：在奖励不可观测时学习激励
When the Agent Learns Too: Learning to Incentivize with Unobserved Rewards
AI 导读
研究提出一种在奖励不可观测场景下的重复逆向选择博弈框架：自利的学习型智能体用多臂老虎机（MAB）问题最大化期望奖励加激励，学习型委托方则并行训练算法，在估计智能体未知奖励与通过自适应激励最大化自身效用之间权衡。
正文 · 原文
Abstract:In practice, incentive providers (i.e., principals) often cannot observe the reward realizations of incentivized agents, which is in contrast to many principal-agent models that have been previously studied. This information asymmetry challenges the principal to consistently estimate the agent's unknown rewards by solely watching the agent's decisions, which becomes even more challenging when the agent has to learn its own rewards. This complex setting is observed in various real-life scenarios ranging from renewable energy storage contracts to personalized healthcare incentives. Hence, it offers not only interesting theoretical questions but also wide practical relevance. This paper explores a repeated adverse selection game between a self-interested learning agent and a learning principal. The agent tackles a multi-armed bandit (MAB) problem to maximize their expected reward plus incentive. On top of the agent's learning, the principal trains a parallel algorithm and faces a trade-off between consistently estimating the agent's unknown rewards and maximizing their own utility by offering adaptive incentives to lead the agent. For a non-parametric model, we introduce an estimator whose only input is the history of principal's incentives and agent's choices. We unite this estimator with a proposed data-driven incentive policy within a MAB framework. Without restricting the type of the agent's algorithm, we prove finite-sample consistency of the estimator and a rigorous regret bound for the principal by considering the sequential externality imposed by the agent. Lastly, our theoretical results are reinforced by simulations justifying applicability of our framework to green energy aggregator contracts.
| Comments: | 72 pages, 6 figures. Companion paper to arXiv:2304.07407; the overlapping text covers the shared model setup |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (stat.ML) |
| Cite as: | arXiv:2308.06717 [cs.LG] |
| (or arXiv:2308.06717v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2308.06717 arXiv-issued DOI via DataCite |
Submission history
From: Ilgin Dogan [view email]
[v1]
Sun, 13 Aug 2023 08:12:01 UTC (251 KB)
[v2]
Wed, 4 Nov 2026 08:41:35 UTC (226 KB)
来源：arXiv cs.AI · arxiv.org