# Survey on Fair Reinforcement Learning: Theory and Practice

arXiv: https://arxiv.org/abs/2205.10032 · HTML: https://mlsanity.com/paper/2205.10032/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 152 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 30 of 63)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Programmatically Interpretable Reinforcement Learning](https://mlsanity.com/paper/1804.02477/) (2018)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Explainable Reinforcement Learning Through a Causal Lens](https://mlsanity.com/paper/1905.10958/) (2019)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning](https://mlsanity.com/paper/1911.10692/) (2019)
- [Inherent Tradeoffs in Learning Fair Representations](https://mlsanity.com/paper/1906.08386/) (2019)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [The Social Cost of Strategic Classification](https://mlsanity.com/paper/1808.08460/) (2018)
- [Causal Bandits: Learning Good Interventions via Causal Inference](https://mlsanity.com/paper/1606.03203/) (2016)
- [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)
- [Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1712.07294/) (2017)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [On Formalizing Fairness in Prediction with Machine Learning](https://mlsanity.com/paper/1710.03184/) (2017)
- [Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees](https://mlsanity.com/paper/1807.05887/) (2018)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [Online Learning with an Unknown Fairness Metric](https://mlsanity.com/paper/1802.06936/) (2018)

## Built on by (most cited first, 14 of 14)

- [Causal Reinforcement Learning: A Survey](https://mlsanity.com/paper/2307.01452/) (2023)
- [Socially Fair Reinforcement Learning](https://mlsanity.com/paper/2208.12584/) (2022)
- [Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning](https://mlsanity.com/paper/2306.00324/) (2023)
- [FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering](https://mlsanity.com/paper/2209.12756/) (2022)
- [Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges](https://mlsanity.com/paper/2406.06736/) (2024)
- [Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance](https://mlsanity.com/paper/2411.18195/) (2024)
- [DECAF: Learning to be Fair in Multi-agent Resource Allocation](https://mlsanity.com/paper/2502.04281/) (2025)
- [Fair Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/2408.17313/) (2024)
- [A Causal Lens for Learning Long-term Fair Policies](https://mlsanity.com/paper/2506.11242/) (2025)
- [DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learning](https://mlsanity.com/paper/2512.00545/) (2025)
- [Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking](https://mlsanity.com/paper/2404.08006/) (2024)
- [Fair Resource Allocation in Weakly Coupled Markov Decision Processes](https://mlsanity.com/paper/2411.09804/) (2024)
- [The Fair Game: Auditing & Debiasing AI Algorithms Over Time](https://mlsanity.com/paper/2508.06443/) (2025)
- [OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities](https://mlsanity.com/paper/2508.12943/) (2025)

## Abstract (arXiv)

Fairness-aware learning aims at satisfying various fairness constraints in addition to the usual performance criteria via data-driven machine learning techniques. Most of the research in fairness-aware learning employs the setting of fair-supervised learning. However, many dynamic real-world applications can be better modeled using sequential decision-making problems and fair reinforcement learning provides a more suitable alternative for addressing these problems. In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework. We discuss various practical applications in which RL methods have been applied to achieve a fair solution with high accuracy. We further include various facets of the theory of fair reinforcement learning, organizing them into single-agent RL, multi-agent RL, long-term fairness via RL, and offline learning. Moreover, we highlight a few major issues to explore in order to advance the field of fair-RL, namely - i) correcting societal biases, ii) feasibility of group fairness or individual fairness, and iii) explainability in RL. Our work is beneficial for both researchers and practitioners as we discuss articles providing mathematical guarantees as well as articles with empirical studies on real-world problems.