mlsanityData to 14 Oct 2026

Fairness in Reinforcement Learning with Bisimulation Metrics

arXiv 2412.17123 ↗First posted 22 Dec 2024cs.LG cs.CYcited by 1

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

Abstract (arXiv)

Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environments. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.

Key facts. Cited by 1 paper 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 37 of its 72 references that are in the map. First posted on arXiv 22 Dec 2024.

Builds on

37 of 72 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning16 Apr
  2. 2024Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning12 Jan

Other references · 35

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  5. 2020Learning Invariant Representations for Reinforcement Learning without Reconstructioncited by 436
  6. 2015Strategic Classificationcited by 257
  7. 2018Delayed Impact of Fair Machine Learningcited by 184
  8. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  9. 2019Scalable methods for computing state similarity in deterministic Markov Decision Processescited by 133
  10. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 114
  11. 2018The Social Cost of Strategic Classificationcited by 105
  12. 2018On Learning Intrinsic Rewards for Policy Gradient Methodscited by 102
23 more
  1. 2018ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Roboticscited by 98
  2. 2016Fairness in Reinforcement Learningcited by 95
  3. 2020Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewardscited by 60
  4. 2019Algorithms for Fairness in Sequential Decision Makingcited by 56
  5. 2018Learning Optimal Fair Policiescited by 49
  6. 2021Towards Robust Bisimulation Metric Learningcited by 49
  7. 2019Learning Fairness in Multi-Agent Systemscited by 44
  8. 2021Heuristic-Guided Reinforcement Learningcited by 42
  9. 2020Fair Algorithms for Multi-Agent Multi-Armed Banditscited by 41
  10. 2017Scalable Co-Optimization of Morphology and Control in Embodied Machinescited by 40
  11. 2022Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learningcited by 38
  12. 2023Long-Term Fairness with Unknown Dynamicscited by 27
  13. 2023SoftZoo: A Soft Robot Co-design Benchmark For Locomotion In Diverse Environmentscited by 20
  14. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 19
  15. 2020My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Banditscited by 16
  16. 2022Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systemscited by 14
  17. 2022Socially Fair Reinforcement Learningcited by 13
  18. 2023Fair Off-Policy Learning from Observational Datacited by 11
  19. 2023Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learningcited by 8
  20. 2023Adapting Static Fairness to Sequential Decision-Making: Bias Mitigation Strategies towards Equal Long-term Benefit Ratecited by 6
  21. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
  22. 2022Contextual bandits with concave rewards, and an application to fair rankingcited by 2
  23. 2021Stochastic Spatio-Temporal Optimization for Control and Co-Design of Systems in Robotics and Applied Physicscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Long-term Fairness with Selective Labelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Group Fairness in Multi-Task Reinforcement Learning
  2. 2024Fairness in Reinforcement Learning: A Survey
  3. 2025Fairness-Aware Reinforcement Learning (FAReL): A Framework for Transparent and Balanced Sequential Decision-Making
  4. 2025A Causal Lens for Learning Long-term Fair Policies
  5. 2025MAFE: Enabling Equitable Algorithm Design in Multi-Agent Multi-Stage Decision-Making Systems
  6. 2026Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
  7. 2025Revisiting Bisimulation Metric for Robust Representations in Reinforcement Learning
  8. 2025Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces