AI RESEARCH
Population-Aware Imitation Learning in Mean-field Games with Common Noise
arXiv CS.LG
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ArXi:2605.03357v1 Announce Type: new Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents. In this paper, we address the problem of Imitation Learning (IL) in MFGs subject to common noise, where the population distribution evolves stochastically. This stochasticity compels agents to adopt population-aware policies to respond to aggregate shocks. We formulate two distinct learning objectives: recovering a Nash equilibrium and maximizing performance against an expert population.