Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games

Jiequn Han, Ruimeng Hu
; Proceedings of The First Mathematical and Scientific Machine Learning Conference, PMLR 107:221-245, 2020.

Abstract

We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games. Following the idea of fictitious play, we recast the $N$-player game into $N$ decoupled decision problems (one for each player) and solve them iteratively. The individual decision problem is characterized by a semilinear Hamilton-Jacobi-Bellman equation, to solve which we employ the recently developed deep BSDE method. The resulted algorithm can solve large $N$-player games for which conventional numerical methods would suffer from the curse of dimensionality. Multiple numerical examples involving identical or heterogeneous agents, with risk-neutral or risk-sensitive objectives, are tested to validate the accuracy of the proposed algorithm in large group games. Even for a fifty-player game with the presence of common noise, the proposed algorithm still finds the approximate Nash equilibrium accurately, which, to our best knowledge, is difficult to achieve by other numerical algorithms.

Cite this Paper


BibTeX
@InProceedings{pmlr-v107-han20a, title = {Deep Fictitious Play for Finding {M}arkovian {N}ash Equilibrium in Multi-Agent Games}, author = {Han, Jiequn and Hu, Ruimeng}, booktitle = {Proceedings of The First Mathematical and Scientific Machine Learning Conference}, pages = {221--245}, year = {2020}, editor = {Jianfeng Lu and Rachel Ward}, volume = {107}, series = {Proceedings of Machine Learning Research}, address = {Princeton University, Princeton, NJ, USA}, month = {20--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v107/han20a/han20a.pdf}, url = {http://proceedings.mlr.press/v107/han20a.html}, abstract = {We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games. Following the idea of fictitious play, we recast the $N$-player game into $N$ decoupled decision problems (one for each player) and solve them iteratively. The individual decision problem is characterized by a semilinear Hamilton-Jacobi-Bellman equation, to solve which we employ the recently developed deep BSDE method. The resulted algorithm can solve large $N$-player games for which conventional numerical methods would suffer from the curse of dimensionality. Multiple numerical examples involving identical or heterogeneous agents, with risk-neutral or risk-sensitive objectives, are tested to validate the accuracy of the proposed algorithm in large group games. Even for a fifty-player game with the presence of common noise, the proposed algorithm still finds the approximate Nash equilibrium accurately, which, to our best knowledge, is difficult to achieve by other numerical algorithms.} }
Endnote
%0 Conference Paper %T Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games %A Jiequn Han %A Ruimeng Hu %B Proceedings of The First Mathematical and Scientific Machine Learning Conference %C Proceedings of Machine Learning Research %D 2020 %E Jianfeng Lu %E Rachel Ward %F pmlr-v107-han20a %I PMLR %J Proceedings of Machine Learning Research %P 221--245 %U http://proceedings.mlr.press %V 107 %W PMLR %X We propose a deep neural network-based algorithm to identify the Markovian Nash equilibrium of general large $N$-player stochastic differential games. Following the idea of fictitious play, we recast the $N$-player game into $N$ decoupled decision problems (one for each player) and solve them iteratively. The individual decision problem is characterized by a semilinear Hamilton-Jacobi-Bellman equation, to solve which we employ the recently developed deep BSDE method. The resulted algorithm can solve large $N$-player games for which conventional numerical methods would suffer from the curse of dimensionality. Multiple numerical examples involving identical or heterogeneous agents, with risk-neutral or risk-sensitive objectives, are tested to validate the accuracy of the proposed algorithm in large group games. Even for a fifty-player game with the presence of common noise, the proposed algorithm still finds the approximate Nash equilibrium accurately, which, to our best knowledge, is difficult to achieve by other numerical algorithms.
APA
Han, J. & Hu, R.. (2020). Deep Fictitious Play for Finding Markovian Nash Equilibrium in Multi-Agent Games. Proceedings of The First Mathematical and Scientific Machine Learning Conference, in PMLR 107:221-245

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