# Learning Fairness in Multi-Agent Systems

arXiv: https://arxiv.org/abs/1910.14472 · HTML: https://mlsanity.com/paper/1910.14472/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 30 of 44)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Virtual Agent Economies](https://mlsanity.com/paper/2509.10147/) (2025)
- [FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT](https://mlsanity.com/paper/2103.16033/) (2021)
- [Survey on Fair Reinforcement Learning: Theory and Practice](https://mlsanity.com/paper/2205.10032/) (2022)
- [Socially Fair Reinforcement Learning](https://mlsanity.com/paper/2208.12584/) (2022)
- [Fair Off-Policy Learning from Observational Data](https://mlsanity.com/paper/2303.08516/) (2023)
- [Recommendation Fairness: From Static to Dynamic](https://mlsanity.com/paper/2109.03150/) (2021)
- [Fairness in Preference-based Reinforcement Learning](https://mlsanity.com/paper/2306.09995/) (2023)
- [Fairness in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2405.06909/) (2024)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards](https://mlsanity.com/paper/2408.12112/) (2024)
- [Cooperative Multi-Agent Fairness and Equivariant Policies](https://mlsanity.com/paper/2106.05727/) (2021)
- [Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning](https://mlsanity.com/paper/2306.00324/) (2023)
- [Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework](https://mlsanity.com/paper/2505.12001/) (2025)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Mediated Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.08419/) (2023)
- [Cooperation and Fairness in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.14916/) (2024)
- [DECAF: Learning to be Fair in Multi-agent Resource Allocation](https://mlsanity.com/paper/2502.04281/) (2025)
- [A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control](https://mlsanity.com/paper/2503.23626/) (2025)
- [Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcare](https://mlsanity.com/paper/2508.18708/) (2025)
- [Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game](https://mlsanity.com/paper/2206.01451/) (2022)
- [Fairness Aware Reinforcement Learning via Proximal Policy Optimization](https://mlsanity.com/paper/2502.03953/) (2025)
- [HypRL: Reinforcement Learning of Control Policies for Hyperproperties](https://mlsanity.com/paper/2504.04675/) (2025)
- [Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2606.18111/) (2026)
- [Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.06800/) (2022)
- [FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systems](https://mlsanity.com/paper/2307.05857/) (2023)
- [Temporal Fairness in Decision Making Problems](https://mlsanity.com/paper/2408.13208/) (2024)
- [Fairness in Reinforcement Learning with Bisimulation Metrics](https://mlsanity.com/paper/2412.17123/) (2024)
- [Past-Discounting is Key for Learning Markovian Fairness with Long Horizons](https://mlsanity.com/paper/2504.01154/) (2025)
- [Fair Contracts in Principal-Agent Games with Heterogeneous Types](https://mlsanity.com/paper/2506.15887/) (2025)

## Abstract (arXiv)

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.