mlsanityData to 14 Oct 2026

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

arXiv 2206.03883 ↗First posted 8 Jun 2022cs.LGcited by 11

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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.

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

11 of 30 references.

References · 11

  1. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  2. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 78
  3. 2019Combinatorial Sleeping Bandits with Fairness Constraintscited by 68
  4. 2020Collapsing Bandits and Their Application to Public Health Interventionscited by 47
  5. 2020Whittle index based Q-learning for restless bandits with average rewardcited by 45
  6. 2019Fair Contextual Multi-Armed Bandits: Theory and Experimentscited by 42
  7. 2021Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcarecited by 35
  8. 2017An Asymptotically Optimal Index Policy for Finite-Horizon Restless Banditscited by 20
  9. 2016Optimal Recommendation to Users that React: Online Learning for a Class of POMDPscited by 13
  10. 2021CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networkscited by 7
  11. 2021Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problemscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 3; 2026: 2.

11 papers.

  1. 2024A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Healthcited by 24
  2. 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approachcited by 12
  3. 2024Allocation Requires Prediction Only if Inequality Is Lowcited by 8
  4. 2023Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Healthcited by 6
  5. 2022Towards Soft Fairness in Restless Multi-Armed Banditscited by 5
  6. 2024Fair Resource Allocation in Weakly Coupled Markov Decision Processescited by 0
  7. 2025Contextual Budget Bandit for Food Rescue Volunteer Engagementcited by 0
  8. 2025MARBLE: Multi-Armed Restless Bandits in Latent Markovian Environmentcited by 0
  9. 2025Networked Restless Multi-Arm Bandits with Reinforcement Learningcited by 0
  10. 2026Long-term Fairness with Selective Labelscited by 0
  11. 2026Fair Policy Optimization in Major-Minor Weakly Coupled Markov Decision Processescited by 0