# Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2206.03883 · HTML: https://mlsanity.com/paper/2206.03883/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Collapsing Bandits and Their Application to Public Health Interventions](https://mlsanity.com/paper/2007.04432/) (2020)
- [Whittle index based Q-learning for restless bandits with average reward](https://mlsanity.com/paper/2004.14427/) (2020)
- [Fair Contextual Multi-Armed Bandits: Theory and Experiments](https://mlsanity.com/paper/1912.08055/) (2019)
- [Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare](https://mlsanity.com/paper/2105.07965/) (2021)
- [An Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits](https://mlsanity.com/paper/1707.00205/) (2017)
- [Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs](https://mlsanity.com/paper/1603.09233/) (2016)
- [CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks](https://mlsanity.com/paper/2107.03603/) (2021)
- [Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems](https://mlsanity.com/paper/2103.04730/) (2021)

## Built on by (most cited first, 11 of 11)

- [A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health](https://mlsanity.com/paper/2402.14807/) (2024)
- [Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach](https://mlsanity.com/paper/2303.03300/) (2023)
- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health](https://mlsanity.com/paper/2308.09726/) (2023)
- [Towards Soft Fairness in Restless Multi-Armed Bandits](https://mlsanity.com/paper/2207.13343/) (2022)
- [Fair Resource Allocation in Weakly Coupled Markov Decision Processes](https://mlsanity.com/paper/2411.09804/) (2024)
- [Contextual Budget Bandit for Food Rescue Volunteer Engagement](https://mlsanity.com/paper/2509.10777/) (2025)
- [MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environment](https://mlsanity.com/paper/2511.09324/) (2025)
- [Networked Restless Multi-Arm Bandits with Reinforcement Learning](https://mlsanity.com/paper/2512.06274/) (2025)
- [Long-term Fairness with Selective Labels](https://mlsanity.com/paper/2605.22291/) (2026)
- [Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processes](https://mlsanity.com/paper/2609.36174/) (2026)

## Abstract (arXiv)

Restless Multi-Armed Bandits (RMAB) is an apt model to represent decision-making problems in public health interventions (e.g., tuberculosis, maternal, and child care), anti-poaching planning, sensor monitoring, personalized recommendations and many more. Existing research in RMAB has contributed mechanisms and theoretical results to a wide variety of settings, where the focus is on maximizing expected value. In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value. In the context of public health settings, this would ensure that different people and/or communities are fairly represented while making public health intervention decisions. To achieve this goal, we formally define the fairness constraints in RMAB and provide planning and learning methods to solve RMAB in a fair manner. We demonstrate key theoretical properties of fair RMAB and experimentally demonstrate that our proposed methods handle fairness constraints without sacrificing significantly on solution quality.