Deep Learning for Dynamic Programming with Recursive Utility
We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is numerically challenging because the recursive utility does not have an explicit representation and the Bellman equation contains a certainty equivalent that is difficult to evaluate. The CEL algorithm learns this certainty-equivalent value directly with neural networks and jointly approximates value functions, policy functions, and certainty-equivalent functions. The CEL algorithm is mesh-free and simulation-based, allowing high-dimensional state and con...
| Authors | Xianhua Peng, Wu Guo |
|---|---|
| Primary category | q-fin.CP |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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