A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909418053959680 |
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| author | Peng, Xianhua Kou, Steven Zhang, Lekang |
| author_facet | Peng, Xianhua Kou, Steven Zhang, Lekang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08668 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics Peng, Xianhua Kou, Steven Zhang, Lekang General Economics Economics Optimization and Control Machine Learning 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. |
| title | A Machine Learning Algorithm for Finite-Horizon Stochastic Control Problems in Economics |
| topic | General Economics Economics Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2411.08668 |