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Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Numerical Applications

Author

Listed:
  • Achref Bachouch

    (Mälardalen University)

  • Côme Huré

    (LPSM, University Paris Diderot)

  • Nicolas Langrené

    (RiskLab Australia)

  • Huyên Pham

    (LPSM, University Paris Diderot
    CREST-ENSAE)

Abstract

This paper presents several numerical applications of deep learning-based algorithms for discrete-time stochastic control problems in finite time horizon that have been introduced in Huré et al. (2018). Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from Weinan et al. (2017) and on quadratic backward stochastic differential equations as in Chassagneux and Richou (2016). We also performed tests on low-dimension control problems such as an option hedging problem in finance, as well as energy storage problems arising in the valuation of gas storage and in microgrid management. Numerical results and comparisons to quantization-type algorithms Qknn, as an efficient algorithm to numerically solve low-dimensional control problems, are also provided.

Suggested Citation

  • Achref Bachouch & Côme Huré & Nicolas Langrené & Huyên Pham, 2022. "Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Numerical Applications," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 143-178, March.
    • Handle: RePEc:spr:metcap:v:24:y:2022:i:1:d:10.1007_s11009-019-09767-9
    DOI: 10.1007/s11009-019-09767-9
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    References listed on IDEAS

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    1. Daniel R. Jiang & Warren B. Powell, 2015. "An Approximate Dynamic Programming Algorithm for Monotone Value Functions," Operations Research, INFORMS, vol. 63(6), pages 1489-1511, December.
    2. Rene Carmona & Michael Ludkovski, 2010. "Valuation of energy storage: an optimal switching approach," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 359-374.
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    Cited by:

    1. Pierre Bras & Gilles Pag`es, 2022. "Langevin algorithms for Markovian Neural Networks and Deep Stochastic control," Papers 2212.12018, arXiv.org, revised Jan 2023.
    2. Alexandre Roch, 2023. "Optimal Liquidation Through a Limit Order Book: A Neural Network and Simulation Approach," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-29, March.
    3. Pierre Bras & Gilles Pagès, 2022. "Langevin algorithms for Markovian Neural Networks and Deep Stochastic control," Working Papers hal-03980632, HAL.
    4. Olivier Bokanowski & Averil Prost & Xavier Warin, 2023. "Neural networks for first order HJB equations and application to front propagation with obstacle terms," Partial Differential Equations and Applications, Springer, vol. 4(5), pages 1-36, October.
    5. Laurens Van Mieghem & Antonis Papapantoleon & Jonas Papazoglou-Hennig, 2023. "Machine learning for option pricing: an empirical investigation of network architectures," Papers 2307.07657, arXiv.org.

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