mlsanityData to 14 Oct 2026

Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables

arXiv 2509.03845 ↗First posted 4 Sep 2025cs.LG cs.AI cs.GTcited by 3

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

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Abstract (arXiv)

Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games (MFGs) offers a practical framework to infer reward functions from expert demonstrations. While promising, the assumption of agent homogeneity limits the capability of existing methods to handle demonstrations with heterogeneous and unknown objectives, which are common in practice. To this end, we propose a deep latent variable MFG model and an associated IRL method. Critically, our method can infer rewards from different yet structurally similar tasks without prior knowledge about underlying contexts or modifying the MFG model itself. Our experiments, conducted on simulated scenarios and a real-world spatial taxi-ride pricing problem, demonstrate the superiority of our approach over state-of-the-art IRL methods in MFGs.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 16 of its 41 references that are in the map. First posted on arXiv 4 Sep 2025.

Builds on

16 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variables20 Sep
  2. 2019Multi-Agent Adversarial Inverse Reinforcement Learning30 Jul
  3. 2018Learning a Prior over Intent via Meta-Inverse Reinforcement Learning31 May

Other references · 13

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 503
  3. 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 389
  4. 2016Learning to reinforcement learncited by 377
  5. 2018Mean Field Multi-Agent Reinforcement Learningcited by 207
  6. 2017Learning to Optimize: Training Deep Neural Networks for Wireless Resource Managementcited by 96
  7. 2021Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learningcited by 73
  8. 2019On the Convergence of Model Free Learning in Mean Field Gamescited by 48
  9. 2015Learning in Mean Field Games: the Fictitious Playcited by 38
  10. 2020Multi Type Mean Field Reinforcement Learningcited by 26
  11. 2017A Simple Neural Attentive Meta-Learnercited by 23
  12. 2018The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modelscited by 5
1 more
  1. 2020Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agentscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Trust Region Reward Optimization and Proximal Inverse Reward Optimization Algorithmcited by 1
  2. 2026Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learningcited by 1
  3. 2026Population-Aware Imitation Learning in Mean-field Games with Common Noisecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Individual-Level Inverse Reinforcement Learning for Mean Field Games
  2. 2021Adversarial Inverse Reinforcement Learning for Mean Field Games
  3. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  4. 2026Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward
  5. 2025Kernel Based Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games
  6. 2023Curricular Subgoals for Inverse Reinforcement Learning
  7. 2021Deep Adaptive Multi-Intention Inverse Reinforcement Learning
  8. 2018Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations