We study fairness through the lens of cooperative multi-agent learning. Our work is motivated by empirical evidence that naive maximization of team reward yields unfair outcomes for individual team members. To address fairness in multi-agent contexts, we introduce team fairness, a group-based fairness measure for multi-agent learning. We then prove that it is possible to enforce team fairness during policy optimization by transforming the team's joint policy into an equivariant map. We refer to our multi-agent learning strategy as Fairness through Equivariance (Fair-E) and demonstrate its effectiveness empirically. We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that it reaches higher levels of utility than Fair-E and fairer outcomes than non-equivariant policies. Finally, we present novel findings regarding the fairness-utility trade-off in multi-agent settings; showing that the magnitude of the trade-off is dependent on agent skill level.
翻译:我们从合作性多试剂学习的角度研究公平问题。我们的工作动力是经验证据,证明对团队奖励的天真最大化会给团队个别成员带来不公平的结果。为了解决多试剂方面的公平问题,我们引入团队公平,这是针对多试剂学习的基于集体的公平措施。然后我们证明,在政策优化期间,可以通过将团队的共同政策转化为平衡性地图来实施团队公平。我们把多试剂学习战略称为公平性(公平-E),并用经验来展示其有效性。然后我们引入公平性(公平-ER)作为公平性(公平-公平-ER)的软约束版,表明公平性(公平-公平-公平)比公平性(公平-公平-公平)和公平性结果比非平等性政策更高。最后,我们提出了关于多试剂环境下公平性交易的新结论;我们表明,交易的规模取决于代理人的技能水平。