Extensions of the deep Galerkin method

Ali Al-Aradi, Adolfo Correia, Gabriel Jardim, Danilo de Freitas Naiff and Yuri Saporito

Applied Mathematics and Computation, 2022, vol. 430, issue C

Abstract: We extend the Deep Galerkin Method (DGM) introduced in Sirignano and Spiliopoulos(2018)[25] to solve a number of partial differential equations (PDEs) that arise in the context of optimal stochastic control and mean field games. First, we consider PDEs where the function is constrained to be positive and integrate to unity, as is the case with Fokker–Planck equations. Our approach involves reparameterizing the solution as the exponential of a neural network appropriately normalized to ensure both requirements are satisfied. This then gives rise to nonlinear a partial integro-differential equation (PIDE) where the integral appearing in the equation is handled by a novel application of importance sampling. Secondly, we tackle a number of Hamilton–Jacobi–Bellman (HJB) equations that appear in stochastic optimal control problems. The key contribution is that these equations are approached in their unsimplified primal form which includes an optimization problem as part of the equation. We extend the DGM algorithm to solve for the value function and the optimal control simultaneously by characterizing both as deep neural networks. Training the networks is performed by taking alternating stochastic gradient descent steps for the two functions, a technique inspired by the policy improvement algorithms (PIA).

Keywords: Partial differential equations; Stochastic control; Hamilton–Jacobi–Bellman equations; Deep Galerkin method; Neural networks; Policy improvement (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:430:y:2022:i:c:s0096300322003617

DOI: 10.1016/j.amc.2022.127287

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