Abstract: 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 incorporate team fairness into policy
optimization -- introducing Fairness through Equivariance (Fair-E), a novel
learning strategy that achieves provably fair reward distributions. We then
introduce Fairness through Equivariance Regularization (Fair-ER) as a
soft-constraint version of Fair-E and show that Fair-ER reaches higher levels
of utility than Fair-E and fairer outcomes than policies with no equivariance.
Finally, we investigate the fairness-utility trade-off in multi-agent settings.