A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics
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Abstract (arXiv)
We propose a machine learning algorithm for solving finite-horizon stochastic control problems based on a deep neural network representation of the optimal policy functions. The algorithm has three features: (1) It can solve high-dimensional (e.g., over 100 dimensions) and finite-horizon time-inhomogeneous stochastic control problems. (2) It has a monotonicity of performance improvement in each iteration, leading to good convergence properties. (3) It does not rely on the Bellman equation. To demonstrate the efficiency of the algorithm, it is applied to solve various finite-horizon time-inhomogeneous problems including recursive utility optimization under a stochastic volatility model, a multi-sector stochastic growth, and optimal control under a dynamic stochastic integration of climate and economy model with eight-dimensional state vectors and 600 time periods.
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References · 6
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- 2017Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equationscited by 65
- 2016Deep Learning Approximation for Stochastic Control Problemscited by 55
- 2018Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysiscited by 24
- 2021DeepHAM: A Global Solution Method for Heterogeneous Agent Models with Aggregate Shockscited by 11
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