mlsanityData to 7 Oct 2026

Deep Learning Approximation for Stochastic Control Problems

arXiv 1611.07422 ↗First posted 2 Nov 2016cs.LG cs.AI cs.NEcited by 51

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Abstract (arXiv)

Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2016.

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Citing papers per year: 2021: 9; 2022: 14; 2023: 7; 2024: 9; 2025: 9; 2026: 3.

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  23. 2022On Parametric Optimal Execution and Machine Learning Surrogatescited by 1
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  27. 2024An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Controlcited by 1
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  37. 2025Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricingcited by 0
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