让自利的博弈方诚实汇报，实现近最优学习效果。
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
- 设计新机制促使各臂主动披露真实收益
- 算法可逼近次优臂的平均真实收益
- 适合研究激励相容与在线学习交叉场景
我们研究带有策略性臂的经典多臂赌博机问题。每个臂具有有界奖励分布，为最大化自身利益可能保留部分收益，仅向学习者披露一部分。该过程在T轮中形成目标冲突：学习者追求最小化累积遗憾，而臂则希望最大化自身效用。为此，我们提出一种新机制，使各臂在均衡下选择如实披露收益。在此机制下，学习者可获得次高真实平均收益，累积遗憾为O(log(T)/Δ)（依赖问题）或O(√(T log(T)))（最坏情况）。
原文摘要 · Abstract (English)
We consider the classical multi-armed bandit problem, but with strategic arms. In this context, each arm is characterized by a bounded support reward distribution and strategically aims to maximize its own utility by potentially retaining a portion of its reward, and disclosing only a fraction of it to the learning agent. This scenario unfolds as a game over $T$ rounds, leading to a competition of objectives between the learning agent, aiming to minimize their regret, and the arms, motivated by the desire to maximize their individual utilities. To address these dynamics, we introduce a new mechanism that establishes an equilibrium wherein each arm behaves truthfully and discloses as much of its rewards as possible. With this mechanism, the agent can attain the second-highest average (true) reward among arms, with a cumulative regret bounded by $O(\log(T)/Δ)$ (problem-dependent) or $O(\sqrt{T\log(T)})$ (worst-case).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。