mlsanityData to 14 Oct 2026

Fairness in Reinforcement Learning: A Survey

arXiv 2405.06909 ↗First posted 11 May 2024cs.LG cs.AI cs.CYcited by 9

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

While our understanding of fairness in machine learning has significantly progressed, our understanding of fairness in reinforcement learning (RL) remains nascent. Most of the attention has been on fairness in one-shot classification tasks; however, real-world, RL-enabled systems (e.g., autonomous vehicles) are much more complicated in that agents operate in dynamic environments over a long period of time. To ensure the responsible development and deployment of these systems, we must better understand fairness in RL. In this paper, we survey the literature to provide the most up-to-date snapshot of the frontiers of fairness in RL. We start by reviewing where fairness considerations can arise in RL, then discuss the various definitions of fairness in RL that have been put forth thus far. We continue to highlight the methodologies researchers used to implement fairness in single- and multi-agent RL systems before showcasing the distinct application domains that fair RL has been investigated in. Finally, we critically examine gaps in the literature, such as understanding fairness in the context of RLHF, that still need to be addressed in future work to truly operationalize fair RL in real-world systems.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 29 of its 66 references that are in the map. First posted on arXiv 11 May 2024.

Builds on

29 of 66 references.

References · 29

  1. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 264
  4. 2017A Convex Framework for Fair Regressioncited by 148
  5. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
  6. 2024MaxMin-RLHF: Alignment with Diverse Human Preferencescited by 123
  7. 2016Fairness in Reinforcement Learningcited by 95
  8. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 78
  9. 2018Online Learning with an Unknown Fairness Metriccited by 62
  10. 2020Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewardscited by 60
  11. 2018Learning Optimal Fair Policiescited by 49
  12. 2019Learning Fairness in Multi-Agent Systemscited by 44
17 more
  1. 2017Calibrated Fairness in Banditscited by 43
  2. 2020Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learningcited by 40
  3. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  4. 2020Fairness in Learning-Based Sequential Decision Algorithms: A Surveycited by 35
  5. 2024Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedbackcited by 32
  6. 2023Runtime Monitoring of Dynamic Fairness Propertiescited by 17
  7. 2019Reinforcement Learning for Joint Optimization of Multiple Rewardscited by 15
  8. 2021FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoTcited by 15
  9. 2014Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Materialcited by 14
  10. 2017The impossibility of "fairness": a generalized impossibility result for decisionscited by 12
  11. 2021Recommendation Fairness: From Static to Dynamiccited by 10
  12. 2019Fair treatment allocations in social networkscited by 9
  13. 2021Cooperative Multi-Agent Fairness and Equivariant Policiescited by 8
  14. 2019Fairness in Multi-agent Reinforcement Learning for Stock Tradingcited by 5
  15. 2019Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammatescited by 4
  16. 2021Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Banditscited by 2
  17. 2023Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistancecited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 5; 2026: 3.

9 papers.

  1. 2025Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessingcited by 5
  2. 2025Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spacescited by 5
  3. 2025Fairness Aware Reinforcement Learning via Proximal Policy Optimizationcited by 2
  4. 2026Fairness under uncertainty in sequential decisionscited by 2
  5. 2024Fair Resource Allocation in Weakly Coupled Markov Decision Processescited by 0
  6. 2025MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systemscited by 0
  7. 2025FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Marketscited by 0
  8. 2026Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applicationscited by 0
  9. 2026Price of Fairness in Short-Term and Long-Term Algorithmic Selectionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Group Fairness in Multi-Task Reinforcement Learning
  2. 2024Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
  3. 2025PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
  4. 2023Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
  5. 2024Balancing the Scales: Reinforcement Learning for Fair Classification
  6. 2026FairTest: Search-Based Fairness Testing for Multi-Agent Reinforcement Learning Systems
  7. 2024What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
  8. 2025Towards Reward Fairness in RLHF: From a Resource Allocation Perspective