# APAC-Net: Alternating the Population and Agent Control via Two Neural Networks to Solve High-Dimensional Stochastic Mean Field Games

@article{Lin2020APACNetAT, title={APAC-Net: Alternating the Population and Agent Control via Two Neural Networks to Solve High-Dimensional Stochastic Mean Field Games}, author={A. T. Lin and Samy Wu Fung and Wuchen Li and L. Nurbekyan and S. Osher}, journal={ArXiv}, year={2020}, volume={abs/2002.10113} }

We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances MFGs that are beyond reach with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle point problem. Second, we parameterize the value and density functions by two neural… CONTINUE READING

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 32 REFERENCES

A machine learning framework for solving high-dimensional mean field game and mean field control problems

- Computer Science, Mathematics
- 2020

16

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

- Mathematics, Computer Science
- 2019

213