  Fairness is essential for human society, contributing to stability and
productivity. Similarly, fairness is also the key for many multi-agent systems.
Taking fairness into multi-agent learning could help multi-agent systems become
both efficient and stable. However, learning efficiency and fairness
simultaneously is a complex, multi-objective, joint-policy optimization. To
tackle these difficulties, we propose FEN, a novel hierarchical reinforcement
learning model. We first decompose fairness for each agent and propose
fair-efficient reward that each agent learns its own policy to optimize. To
avoid multi-objective conflict, we design a hierarchy consisting of a
controller and several sub-policies, where the controller maximizes the
fair-efficient reward by switching among the sub-policies that provides diverse
behaviors to interact with the environment. FEN can be trained in a fully
decentralized way, making it easy to be deployed in real-world applications.
Empirically, we show that FEN easily learns both fairness and efficiency and
significantly outperforms baselines in a variety of multi-agent scenarios.
