Learning Fairness in Multi-Agent Systems
This addresses fairness and efficiency challenges in multi-agent systems, which is important for applications like robotics or game theory, but appears incremental as it builds on existing hierarchical and reinforcement learning methods.
The paper tackles the problem of learning both fairness and efficiency simultaneously in multi-agent systems, proposing a hierarchical reinforcement learning model called FEN that significantly outperforms baselines in various scenarios.
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.