多智能体博弈中用探查机制平衡公平与效率
Fair Algorithms with Probing for Multi-Agent Multi-Armed Bandits
- 通过策略性探查获取奖励信息，优化资源分配
- 在线场景下实现亚线性后悔率，同时保障公平性
- 适合需要公平决策的多智能体系统应用
我们提出一种多智能体多臂赌博机（MA-MAB）框架，旨在确保各智能体间公平结果的同时最大化系统整体性能。该设定下的核心挑战在于对臂奖励信息的有限了解。为此，我们引入一种新颖的探查框架，在分配前战略性地收集所选臂的信息。在离线设置中，利用子模性设计贪婪探查算法，并获得可证明的性能界；在更复杂的在线设置中，开发出一种算法，在保持公平性的同时实现亚线性后悔。在合成与真实数据集上的大量实验表明，该方法优于基线，兼具更好的公平性与效率。
原文摘要 · Abstract (English)
We propose a multi-agent multi-armed bandit (MA-MAB) framework aimed at ensuring fair outcomes across agents while maximizing overall system performance. A key challenge in this setting is decision-making under limited information about arm rewards. To address this, we introduce a novel probing framework that strategically gathers information about selected arms before allocation. In the offline setting, where reward distributions are known, we leverage submodular properties to design a greedy probing algorithm with a provable performance bound. For the more complex online setting, we develop an algorithm that achieves sublinear regret while maintaining fairness. Extensive experiments on synthetic and real-world datasets show that our approach outperforms baseline methods, achieving better fairness and efficiency.
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