Learning Fairness in Multi-Agent Systems
arXiv:1910.14472
Abstract
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.
NeurIPS'19
Cited by in corpus (6)
- Hierarchically Fair Federated Learning
- ROMA: Multi-Agent Reinforcement Learning with Emergent Roles
- Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
- Recommendation Fairness: From Static to Dynamic
- Reinforcement Learning for Joint Optimization of Multiple Rewards
- A Sociotechnical View of Algorithmic Fairness