Deep Learning for Continuous-Time Stochastic Control with Jumps
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914256455204864 |
|---|---|
| author | Cheridito, Patrick Dupret, Jean-Loup Hainaut, Donatien |
| author_facet | Cheridito, Patrick Dupret, Jean-Loup Hainaut, Donatien |
| contents | In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous-time version of the dynamic programming principle, we derive two different training objectives based on the Hamilton-Jacobi-Bellman equation, ensuring that the networks capture the underlying stochastic dynamics. Empirical evaluations on different problems illustrate the accuracy and scalability of our approach, demonstrating its effectiveness in solving complex high-dimensional stochastic control tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Continuous-Time Stochastic Control with Jumps Cheridito, Patrick Dupret, Jean-Loup Hainaut, Donatien Machine Learning Systems and Control Optimization and Control Portfolio Management 93E20, 68T07, 65C30 I.2.8; I.2.6 In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous-time version of the dynamic programming principle, we derive two different training objectives based on the Hamilton-Jacobi-Bellman equation, ensuring that the networks capture the underlying stochastic dynamics. Empirical evaluations on different problems illustrate the accuracy and scalability of our approach, demonstrating its effectiveness in solving complex high-dimensional stochastic control tasks. |
| title | Deep Learning for Continuous-Time Stochastic Control with Jumps |
| topic | Machine Learning Systems and Control Optimization and Control Portfolio Management 93E20, 68T07, 65C30 I.2.8; I.2.6 |
| url | https://arxiv.org/abs/2505.15602 |