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Learning Fairness in Multi-Agent Systems

arXiv 1910.14472 ↗First posted 31 Oct 2019cs.LG cs.AI cs.MAcited by 44

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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.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 5; 2023: 7; 2024: 9; 2025: 11; 2026: 8.

44 papers.

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  1. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  2. 2025Virtual Agent Economiescited by 17
  3. 2021FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoTcited by 15
  4. 2022Survey on Fair Reinforcement Learning: Theory and Practicecited by 14
  5. 2022Socially Fair Reinforcement Learningcited by 13
  6. 2023Fair Off-Policy Learning from Observational Datacited by 11
  7. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  8. 2023Fairness in Preference-based Reinforcement Learningcited by 9
  9. 2024Fairness in Reinforcement Learning: A Surveycited by 9
  10. 2024Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewardscited by 9
  11. 2021Cooperative Multi-Agent Fairness and Equivariant Policiescited by 8
  12. 2023Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learningcited by 8
32 more
  1. 2024Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contractscited by 8
  2. 2025Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Frameworkcited by 6
  3. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  4. 2024Cooperation and Fairness in Multi-Agent Reinforcement Learningcited by 5
  5. 2023Mediated Multi-Agent Reinforcement Learningcited by 4
  6. 2025DECAF: Learning to be Fair in Multi-agent Resource Allocationcited by 4
  7. 2025A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Controlcited by 3
  8. 2025Skill-Aligned Fairness in Multi-Agent Learning for Collaboration in Healthcarecited by 3
  9. 2022Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Gamecited by 2
  10. 2025Fairness Aware Reinforcement Learning via Proximal Policy Optimizationcited by 2
  11. 2025HypRL: Reinforcement Learning of Control Policies for Hyperpropertiescited by 2
  12. 2026Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learningcited by 2
  13. 2022Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learningcited by 1
  14. 2024Temporal Fairness in Decision Making Problemscited by 1
  15. 2024Fairness in Reinforcement Learning with Bisimulation Metricscited by 1
  16. 2025Past-Discounting is Key for Learning Markovian Fairness with Long Horizonscited by 1
  17. 2025Fair Contracts in Principal-Agent Games with Heterogeneous Typescited by 1
  18. 2026Altruism and Fair Objective in Mixed-Motive Markov gamescited by 1
  19. 2026The Coordination Gap: Multi-Agent Alternation Metrics for Temporal Fairness in Repeated Gamescited by 1
  20. 2022Offsetting Unequal Competition through RL-assisted Incentive Schemescited by 0
  21. 2023FAIRO: Fairness-aware Adaptation in Sequential-Decision Making for Human-in-the-Loop Systemscited by 0
  22. 2023FinA: Fairness of Adverse Effects in Decision-Making of Human-Cyber-Physical-Systemcited by 0
  23. 2024General Automatic Solution Generation of Social Problemscited by 0
  24. 2024Using Protected Attributes to Consider Fairness in Multi-Agent Systemscited by 0
  25. 2024Fair Resource Allocation in Weakly Coupled Markov Decision Processescited by 0
  26. 2025MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systemscited by 0
  27. 2025A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocationcited by 0
  28. 2026On Gossip Algorithms for Machine Learning with Pairwise Objectivescited by 0
  29. 2026α-fair heterogeneous agent reinforcement learningcited by 0
  30. 2026The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordinationcited by 0
  31. 2026Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordinationcited by 0
  32. 2026Inference-Time Policy Alignment for Fair Reinforcement Learningcited by 0