mlsanityData to 7 Oct 2026

Mean Field Correlated Imitation Learning

arXiv 2404.09324 ↗First posted 14 Apr 2024cs.MAcited by 0

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

Abstract (arXiv)

We investigate multi-agent imitation learning (IL) within the framework of mean field games (MFGs), considering the presence of time-varying correlated signals. Existing MFG IL algorithms assume demonstrations are sampled from Mean Field Nash Equilibria (MFNE), limiting their adaptability to real-world scenarios. For example, in the traffic network equilibrium influenced by public routing recommendations, recommendations introduce time-varying correlated signals into the game, not captured by MFNE and other existing correlated equilibrium concepts. To address this gap, we propose Adaptive Mean Field Correlated Equilibrium (AMFCE), a general equilibrium incorporating time-varying correlated signals. We establish the existence of AMFCE under mild conditions and prove that MFNE is a subclass of AMFCE. We further propose Correlated Mean Field Imitation Learning (CMFIL), a novel IL framework designed to recover the AMFCE, accompanied by a theoretical guarantee on the quality of the recovered policy. Experimental results, including a real-world traffic flow prediction problem, demonstrate the superiority of CMFIL over state-of-the-art IL baselines, highlighting the potential of CMFIL in understanding large population behavior under correlated signals.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 30 references that are in the map. First posted on arXiv 14 Apr 2024.

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13 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Adversarial Inverse Reinforcement Learning for Mean Field Games29 Apr
  2. 2019Multi-Agent Adversarial Inverse Reinforcement Learning30 Jul

Other references · 11

  1. 2018Which Training Methods for GANs do actually Converge?cited by 672
  2. 2018Mean Field Multi-Agent Reinforcement Learningcited by 202
  3. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  4. 2021Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learningcited by 65
  5. 2020Fictitious Play for Mean Field Games: Continuous Time Analysis and Applicationscited by 57
  6. 2022Learning in Mean Field Games: A Surveycited by 56
  7. 2020Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPUcited by 45
  8. 2021Mean Field Games Flock! The Reinforcement Learning Waycited by 30
  9. 2013Computational Rationalization: The Inverse Equilibrium Problemcited by 24
  10. 2021Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSROcited by 13
  11. 2020Correlated equilibria and mean field games: a simple modelcited by 6

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  1. 2023On Imitation in Mean-field Games
  2. 2026Population-Aware Imitation Learning in Mean-field Games with Common Noise
  3. 2022Individual-Level Inverse Reinforcement Learning for Mean Field Games
  4. 2025Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
  5. 2026Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning
  6. 2025Kernel Based Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games
  7. 2026Multi-agent imitation learning with function approximation: Linear Markov games and beyond
  8. 2025Learning Equilibria from Data: Provably Efficient Multi-Agent Imitation Learning