Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: We consider the problem of representing a large population's behavior policy that drives the evolution of the 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 individual 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.
  • TL;DR: Inference of a mean field game (MFG) model of large population behavior via a synthesis of MFG and Markov decision processes.
  • Keywords: mean field games, reinforcement learning, Markov decision processes, inverse reinforcement learning, deep learning, inverse optimal control, computational social science, population modeling

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