# Fairness in Reinforcement Learning: A Survey

arXiv: https://arxiv.org/abs/2405.06909 · HTML: https://mlsanity.com/paper/2405.06909/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 29 of 29)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [A Survey of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2312.14925/) (2023)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [MaxMin-RLHF: Alignment with Diverse Human Preferences](https://mlsanity.com/paper/2402.08925/) (2024)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Online Learning with an Unknown Fairness Metric](https://mlsanity.com/paper/1802.06936/) (2018)
- [Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards](https://mlsanity.com/paper/2008.07773/) (2020)
- [Learning Optimal Fair Policies](https://mlsanity.com/paper/1809.02244/) (2018)
- [Learning Fairness in Multi-Agent Systems](https://mlsanity.com/paper/1910.14472/) (2019)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.09421/) (2020)
- [Personalized Counterfactual Fairness in Recommendation](https://mlsanity.com/paper/2105.09829/) (2021)
- [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020)
- [Provable Pluralistic Alignment: Multi-Party RLHF under Offline Human Feedback](https://mlsanity.com/paper/2403.05006/) (2024)
- [Runtime Monitoring of Dynamic Fairness Properties](https://mlsanity.com/paper/2305.04699/) (2023)
- [Reinforcement Learning for Joint Optimization of Multiple Rewards](https://mlsanity.com/paper/1909.02940/) (2019)
- [FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT](https://mlsanity.com/paper/2103.16033/) (2021)
- [Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material](https://mlsanity.com/paper/1406.3497/) (2014)
- [The impossibility of "fairness": a generalized impossibility result for decisions](https://mlsanity.com/paper/1707.01195/) (2017)
- [Recommendation Fairness: From Static to Dynamic](https://mlsanity.com/paper/2109.03150/) (2021)
- [Fair treatment allocations in social networks](https://mlsanity.com/paper/1911.05489/) (2019)
- [Cooperative Multi-Agent Fairness and Equivariant Policies](https://mlsanity.com/paper/2106.05727/) (2021)
- [Fairness in Multi-agent Reinforcement Learning for Stock Trading](https://mlsanity.com/paper/2001.00918/) (2019)
- [Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates](https://mlsanity.com/paper/1907.00313/) (2019)
- [Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Bandits](https://mlsanity.com/paper/2111.14387/) (2021)
- [Fairness-Sensitive Policy-Gradient Reinforcement Learning for Reducing Bias in Robotic Assistance](https://mlsanity.com/paper/2306.04167/) (2023)

## Built on by (most cited first, 9 of 9)

- [Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing](https://mlsanity.com/paper/2501.06366/) (2025)
- [Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces](https://mlsanity.com/paper/2502.11828/) (2025)
- [Fairness Aware Reinforcement Learning via Proximal Policy Optimization](https://mlsanity.com/paper/2502.03953/) (2025)
- [Fairness under uncertainty in sequential decisions](https://mlsanity.com/paper/2604.21711/) (2026)
- [Fair Resource Allocation in Weakly Coupled Markov Decision Processes](https://mlsanity.com/paper/2411.09804/) (2024)
- [MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems](https://mlsanity.com/paper/2502.18534/) (2025)
- [FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets](https://mlsanity.com/paper/2506.22708/) (2025)
- [Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications](https://mlsanity.com/paper/2604.04334/) (2026)
- [Price of Fairness in Short-Term and Long-Term Algorithmic Selections](https://mlsanity.com/paper/2605.06227/) (2026)

## 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.