AI RESEARCH
Recurrent Structural Policy Gradient for Partially Observable Mean Field Games
arXiv CS.AI
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ArXi:2602.20141v2 Announce Type: replace Mean Field Games (MFGs) provide a principled framework for modelling interactions in large population systems. However, algorithmic progress has been limited since model-free methods are high variance and exact methods scale poorly. Recent Hybrid Structural Methods (HSMs) reduce variance while maintaining tractability by leveraging low-dimensional individual state and action spaces and known transition dynamics to compute the exact expected return conditioned on Monte Carlo rollouts of common noise.