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Convergence of deep fictitious play for stochastic differential games

  • * Corresponding author: Ruimeng Hu

    * Corresponding author: Ruimeng Hu 

R.H. was partially supported by the NSF grant DMS-1953035

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  • Stochastic differential games have been used extensively to model agents' competitions in finance, for instance, in P2P lending platforms from the Fintech industry, the banking system for systemic risk, and insurance markets. The recently proposed machine learning algorithm, deep fictitious play, provides a novel and efficient tool for finding Markovian Nash equilibrium of large $ N $-player asymmetric stochastic differential games [J. Han and R. Hu, Mathematical and Scientific Machine Learning Conference, pages 221-245, PMLR, 2020]. By incorporating the idea of fictitious play, the algorithm decouples the game into $ N $ sub-optimization problems, and identifies each player's optimal strategy with the deep backward stochastic differential equation (BSDE) method parallelly and repeatedly. In this paper, we prove the convergence of deep fictitious play (DFP) to the true Nash equilibrium. We can also show that the strategy based on DFP forms an $ \epsilon $-Nash equilibrium. We generalize the algorithm by proposing a new approach to decouple the games, and present numerical results of large population games showing the empirical convergence of the algorithm beyond the technical assumptions in the theorems.

    Mathematics Subject Classification: Primary: 91A15, 68T07;Secondary: 60H10, 60H30.


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  • Figure 1.  A sample path for all $ N = 10 $ players in the inter-bank game, obtained from decoupling the problem by policy update and solving the sub-problems with the Deep BSDE method. Top: the optimal state process $ X_t^i $ (solid lines) and its neural networks approximation $ \hat{X}_t^i $ (circles), under the same realized path of Brownian motion. Bottom: comparisons of the strategies $ \alpha_t^i $ and $ \hat{\alpha}_t^i $ (dashed lines)

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