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Title: Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large popula- tions and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individ- ual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.  more » « less
Award ID(s):
1745382 1620342
NSF-PAR ID:
10112528
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
International Conference on Learning Representations (ICLR)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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